{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np  \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "import glob\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "\n",
    "choice = 'perc02'\n",
    "\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Get LIONESS, SPCC, and SWEET networks\n",
    "\n",
    "Here we describe how to compute and pre-process the other networks.\n",
    "\n",
    "WARNING: most of these steps are memory and time intensive. We have put all the steps here so it would be easier to\n",
    "find, but realistically you might want to split them in separate files. \n",
    "We would recommend you try them \n",
    "out on a smaller amount of samples before running the whole notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# LIONESS\n",
    "def lioness_scaled(data, i):\n",
    "    # Construct sample specific network\n",
    "\n",
    "    corr_all = np.corrcoef(data)\n",
    "    v0 = pd.DataFrame.drop(data, i, axis=1)\n",
    "    corr_single = np.corrcoef(v0)\n",
    "    x = data.shape[1] * (corr_all - corr_single) + corr_single\n",
    "    x[np.isnan(x)] = 0\n",
    "\n",
    "    m_x = np.max(np.abs(x[x != 0]))\n",
    "    x = x/m_x\n",
    "    np.fill_diagonal(x, 1)\n",
    "    \n",
    "    corrnet = x\n",
    "\n",
    "    return corrnet\n",
    "  \n",
    "# LIONESS ORIGINAL\n",
    "def lioness(data, i):\n",
    "    # Construct sample specific network\n",
    "\n",
    "    corr_all = np.corrcoef(data)\n",
    "    v0 = pd.DataFrame.drop(data, i, axis=1)\n",
    "    corr_single = np.corrcoef(v0)\n",
    "    x = data.shape[1] * (corr_all - corr_single) + corr_single\n",
    "    x[np.isnan(x)] = 0\n",
    "\n",
    "    #m_x = np.max(np.abs(x[x != 0]))\n",
    "    #x = x/m_x\n",
    "    #np.fill_diagonal(x, 1)\n",
    "    \n",
    "    corrnet = x\n",
    "\n",
    "    return corrnet \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Single Pearson Correlation Coefficient\n",
    "def spcc(data, i):\n",
    "    # Construct sample specific network\n",
    "    \n",
    "    mean_all = np.mean(data, axis=1)\n",
    "    sd_all = np.std(data, axis=1)\n",
    "    v = data[:, i]\n",
    "    x = (v - mean_all) / sd_all\n",
    "    x[np.isnan(x)] = 0\n",
    "    x[np.abs(x) == np.inf] = 0\n",
    "    prod = np.outer(x, x)\n",
    "    x = prod\n",
    "    m_x = np.max(np.abs(x[x != 0]))\n",
    "    x = x/m_x\n",
    "    np.fill_diagonal(x, 1)\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the SPCC network\n",
    "def get_single_spcc_network(data, sample):\n",
    "    \"\"\"Compute single sample SPCC network\n",
    "\n",
    "    Args:\n",
    "        data (pd.DataFrame): expression dataframe, samples as columns and genes as rows\n",
    "        sample (str): sample name\n",
    "    \"\"\"\n",
    "    # Get the index of the sample\n",
    "    i = data.columns.tolist().index(sample)\n",
    "    # Get the network    \n",
    "    spcc_net = spcc(data.values, i)\n",
    "    # Put in dataframe\n",
    "    temp = pd.DataFrame(spcc_net, index = data.index, columns = data.index)\n",
    "    # Upper triangular matrix\n",
    "    temp = temp.where(np.triu(np.ones(temp.shape), k = 1).astype(bool))\n",
    "    # Put in long format\n",
    "    temp = temp.stack().reset_index() \n",
    "    # Rename columns\n",
    "    temp.columns = ['gene1','gene2',sample]\n",
    "    return(temp)\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_long_network(adj, columns = ['gene1','gene2', 'condition']):\n",
    "    \"\"\"Get long adjacency list from adjacency matrix\n",
    "\n",
    "    Args:\n",
    "        adj (pd.DataFrame): Adjacency matrix as dataframe (square)\n",
    "        columns (list, optional): name of the networks columns. Defaults to ['gene1','gene2', 'condition'].\n",
    "    \"\"\"\n",
    "    adj.index = adj.columns\n",
    "    df_long = adj.where(np.triu(np.ones(adj.shape), k = 1).astype(bool))\n",
    "    df_long = df_long.stack().reset_index() \n",
    "    df_long.columns = columns\n",
    "    return(df_long)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SPCC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "\n",
    "choice = 'nonzero'\n",
    "\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WT(ho)_AmmoniumSulfate</th>\n",
       "      <th>WT(ho)_CStarve</th>\n",
       "      <th>WT(ho)_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalEtOH</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>WT(ho)_Proline</th>\n",
       "      <th>WT(ho)_Urea</th>\n",
       "      <th>WT(ho)_YPD</th>\n",
       "      <th>WT(ho)_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_CStarve</th>\n",
       "      <th>stp2_Glutamine</th>\n",
       "      <th>stp2_MinimalEtOH</th>\n",
       "      <th>stp2_MinimalGlucose</th>\n",
       "      <th>stp2_Proline</th>\n",
       "      <th>stp2_Urea</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>stp2_YPDDiauxic</th>\n",
       "      <th>stp2_YPDRapa</th>\n",
       "      <th>stp2_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL248W</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00489</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL247W</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL246C</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL245C</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.004175</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017700</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL244W</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.012474</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00678</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.022282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.035091</td>\n",
       "      <td>0.012073</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.012945</td>\n",
       "      <td>0.024693</td>\n",
       "      <td>0.061875</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         WT(ho)_AmmoniumSulfate  WT(ho)_CStarve  WT(ho)_Glutamine  \\\n",
       "YDL248W                     0.0        0.000000          0.000000   \n",
       "YDL247W                     0.0        0.000000          0.000000   \n",
       "YDL246C                     0.0        0.000000          0.000000   \n",
       "YDL245C                     0.0        0.004175          0.000000   \n",
       "YDL244W                     0.0        0.012474          0.006734   \n",
       "\n",
       "         WT(ho)_MinimalEtOH  WT(ho)_MinimalGlucose  WT(ho)_Proline  \\\n",
       "YDL248W                 0.0                0.00489        0.015267   \n",
       "YDL247W                 0.0                0.00000        0.000000   \n",
       "YDL246C                 0.0                0.00000        0.000000   \n",
       "YDL245C                 0.0                0.00000        0.000000   \n",
       "YDL244W                 0.0                0.00000        0.000000   \n",
       "\n",
       "         WT(ho)_Urea  WT(ho)_YPD  WT(ho)_YPDDiauxic  WT(ho)_YPDRapa  ...  \\\n",
       "YDL248W      0.00000    0.000000           0.003180        0.002144  ...   \n",
       "YDL247W      0.00000    0.000000           0.000000        0.000000  ...   \n",
       "YDL246C      0.00000    0.000000           0.000000        0.000000  ...   \n",
       "YDL245C      0.00000    0.000000           0.000000        0.002144  ...   \n",
       "YDL244W      0.00678    0.001024           0.028259        0.022282  ...   \n",
       "\n",
       "         stp2_CStarve  stp2_Glutamine  stp2_MinimalEtOH  stp2_MinimalGlucose  \\\n",
       "YDL248W      0.000000             0.0          0.000000             0.004040   \n",
       "YDL247W      0.000000             0.0          0.000000             0.000000   \n",
       "YDL246C      0.000000             0.0          0.000000             0.000000   \n",
       "YDL245C      0.000000             0.0          0.017700             0.000000   \n",
       "YDL244W      0.017778             0.0          0.035091             0.012073   \n",
       "\n",
       "         stp2_Proline  stp2_Urea  stp2_YPD  stp2_YPDDiauxic  stp2_YPDRapa  \\\n",
       "YDL248W      0.009852        0.0  0.000000         0.006494      0.003743   \n",
       "YDL247W      0.000000        0.0  0.000000         0.000000      0.000000   \n",
       "YDL246C      0.000000        0.0  0.000000         0.000000      0.000000   \n",
       "YDL245C      0.000000        0.0  0.000000         0.000000      0.001249   \n",
       "YDL244W      0.009852        0.0  0.001278         0.012945      0.024693   \n",
       "\n",
       "         stp2_YPEtOH  \n",
       "YDL248W     0.000000  \n",
       "YDL247W     0.000000  \n",
       "YDL246C     0.000000  \n",
       "YDL245C     0.000000  \n",
       "YDL244W     0.061875  \n",
       "\n",
       "[5 rows x 132 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read data\n",
    "with open(yeast_expression, 'r') as f:\n",
    "    df = pd.read_csv(f, sep='\\t', index_col=0)\n",
    "    df.index.name = None\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6520, 132)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need to select the sample names divided by media and genetics. \n",
    "This way we are gonna save only the networks based on the genetic background\n",
    "In total we are saving 11 files, instead of 132\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_WT(ho).h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal80.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal81.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal82.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gat1.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gcn4.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gln3.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gzf3.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg1.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg3.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp1.h5\n",
      "../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp2.h5\n"
     ]
    }
   ],
   "source": [
    "ddd = pd.DataFrame()\n",
    "ddd['samples'] = df.columns\n",
    "ddd['media'] = ddd['samples'].str.split('_').str[1]\n",
    "ddd['genetic'] = ddd['samples'].str.split('_').str[0]\n",
    "\n",
    "if os.path.exists(output_folder+'networks/spcc/'):\n",
    "    pass\n",
    "else:\n",
    "    os.makedirs(output_folder+'networks/spcc/')\n",
    "\n",
    "for i,g in ddd.groupby('genetic'):\n",
    "    for k,sample in enumerate(g.samples.tolist()):\n",
    "        if k==0:\n",
    "            spcc_net = get_single_spcc_network(df, sample)\n",
    "        else:\n",
    "            temp = get_single_spcc_network(df, sample)\n",
    "            spcc_net = pd.concat([spcc_net, temp.iloc[:,2]], axis = 1)\n",
    "        if k==len(g.samples.tolist())-1:\n",
    "            spcc_net.to_hdf(output_folder+'networks/spcc/inferelator_spcc_'+i+'.h5', key = 'spcc')\n",
    "            print(output_folder+'networks/spcc/inferelator_spcc_'+i+'.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get the top 1k edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal81.h5\n",
      "1 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp1.h5\n",
      "2 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal80.h5\n",
      "3 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gat1.h5\n",
      "4 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg3.h5\n",
      "5 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp2.h5\n",
      "6 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal82.h5\n",
      "7 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gcn4.h5\n",
      "8 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg1.h5\n",
      "9 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gln3.h5\n",
      "10 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_WT(ho).h5\n",
      "11 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gzf3.h5\n"
     ]
    }
   ],
   "source": [
    "import glob as glob\n",
    "ns = []\n",
    "for i,fn in enumerate(glob.glob(output_folder+'networks/spcc/inferelator_spcc_*.h5')):\n",
    "    print(i, fn)\n",
    "    df = pd.read_hdf(fn, key = 'spcc')\n",
    "    ns.append(len(df.columns)-2)\n",
    "    if i==0:\n",
    "        mysum = df.iloc[:,2:].sum(axis = 1)\n",
    "    else:\n",
    "        mysum += df.iloc[:,2:].sum(axis = 1)\n",
    "\n",
    "# Get top 1k edges\n",
    "mean = pd.Series(mysum/np.sum(ns))\n",
    "mean.sort_values(ascending = False, inplace = True)\n",
    "# Get top 1K\n",
    "top_1k = np.r_[mean.index.values[:500], mean.index.values[-500:]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we re-read the networks and keep only the top1k edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal81.h5\n",
      "1 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp1.h5\n",
      "2 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal80.h5\n",
      "3 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gat1.h5\n",
      "4 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg3.h5\n",
      "5 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp2.h5\n",
      "6 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal82.h5\n",
      "7 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gcn4.h5\n",
      "8 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg1.h5\n",
      "9 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gln3.h5\n",
      "10 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_WT(ho).h5\n",
      "11 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gzf3.h5\n"
     ]
    }
   ],
   "source": [
    "for i,fn in enumerate(glob.glob(output_folder+'networks/spcc/inferelator_spcc_*.h5')):\n",
    "    print(i, fn)\n",
    "    df = pd.read_hdf(fn, key = 'spcc')\n",
    "    if i==0:\n",
    "        spcc_net = df.iloc[top_1k, :]\n",
    "    else:\n",
    "        spcc_net = pd.concat([spcc_net, df.iloc[top_1k, 2:]], axis = 1)\n",
    "\n",
    "spcc_net.to_csv(output_folder+'networks/inferelator_spcc_top1k.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 134)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spcc_net.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spcc_net.iloc[:,2:].mean(axis = 1).hist(bins = 100)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Random Edges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal81.h5\n",
      "1 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp1.h5\n",
      "2 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal80.h5\n",
      "3 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gat1.h5\n",
      "4 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg3.h5\n",
      "5 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_stp2.h5\n",
      "6 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_dal82.h5\n",
      "7 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gcn4.h5\n",
      "8 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_rtg1.h5\n",
      "9 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gln3.h5\n",
      "10 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_WT(ho).h5\n",
      "11 ../results/pseudobulk_nonzero/networks/spcc/inferelator_spcc_gzf3.h5\n"
     ]
    }
   ],
   "source": [
    "for i,fn in enumerate(glob.glob(output_folder+'networks/spcc/inferelator_spcc_*.h5')):\n",
    "    print(i, fn)\n",
    "    df = pd.read_hdf(fn, key = 'spcc')\n",
    "    if i==0:\n",
    "        edges_index = np.random.choice(df.index, 1000, replace = False)\n",
    "        random_net = df.iloc[edges_index, :]\n",
    "    else:\n",
    "        random_net = pd.concat([random_net, df.iloc[edges_index, 2:]], axis = 1)\n",
    "\n",
    "random_net.to_csv(output_folder+'networks/inferelator_spcc_random1k.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "random_net.iloc[:,2:].mean(axis = 1).hist(bins = 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SWEET\n",
    "\n",
    "To compute SWEET networks we are using the github code at [SysMednet/SWEET](https://github.com/SysMednet/SWEET).\n",
    "\n",
    "From there we cloned `1.SWEET_sample_weight_calculating.py` and `2.SWEET_edge_score_calculating.py` inside the src/sweet\n",
    "folder and run the inference as explained by the authors. \n",
    "\n",
    "However, since the zscore computation was extremely computationally heavy, we rewrote the code to make it more\n",
    "efficient. Note that we are not changing any inference step, but only making the reading/saving/storing steps \n",
    "more efficient.\n",
    "\n",
    "#### Create patient and gene files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WT(ho)_AmmoniumSulfate</th>\n",
       "      <th>WT(ho)_CStarve</th>\n",
       "      <th>WT(ho)_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalEtOH</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>WT(ho)_Proline</th>\n",
       "      <th>WT(ho)_Urea</th>\n",
       "      <th>WT(ho)_YPD</th>\n",
       "      <th>WT(ho)_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_CStarve</th>\n",
       "      <th>stp2_Glutamine</th>\n",
       "      <th>stp2_MinimalEtOH</th>\n",
       "      <th>stp2_MinimalGlucose</th>\n",
       "      <th>stp2_Proline</th>\n",
       "      <th>stp2_Urea</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>stp2_YPDDiauxic</th>\n",
       "      <th>stp2_YPDRapa</th>\n",
       "      <th>stp2_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL248W</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL244W</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.012474</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.022282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035091</td>\n",
       "      <td>0.012073</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.012945</td>\n",
       "      <td>0.024693</td>\n",
       "      <td>0.061875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL243C</th>\n",
       "      <td>0.00823</td>\n",
       "      <td>0.044997</td>\n",
       "      <td>0.020068</td>\n",
       "      <td>0.074108</td>\n",
       "      <td>0.014599</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030275</td>\n",
       "      <td>0.111393</td>\n",
       "      <td>0.105790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043867</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.194156</td>\n",
       "      <td>0.035789</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.020254</td>\n",
       "      <td>0.090263</td>\n",
       "      <td>0.113329</td>\n",
       "      <td>0.068520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL241W</th>\n",
       "      <td>0.00823</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013232</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006192</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL240W</th>\n",
       "      <td>0.00823</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.037740</td>\n",
       "      <td>0.019418</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.020203</td>\n",
       "      <td>0.004090</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.018076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.052186</td>\n",
       "      <td>0.027946</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.009725</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.027974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         WT(ho)_AmmoniumSulfate  WT(ho)_CStarve  WT(ho)_Glutamine  \\\n",
       "YDL248W                 0.00000        0.000000          0.000000   \n",
       "YDL244W                 0.00000        0.012474          0.006734   \n",
       "YDL243C                 0.00823        0.044997          0.020068   \n",
       "YDL241W                 0.00823        0.000000          0.000000   \n",
       "YDL240W                 0.00823        0.000000          0.013423   \n",
       "\n",
       "         WT(ho)_MinimalEtOH  WT(ho)_MinimalGlucose  WT(ho)_Proline  \\\n",
       "YDL248W            0.000000               0.004890        0.015267   \n",
       "YDL244W            0.000000               0.000000        0.000000   \n",
       "YDL243C            0.074108               0.014599        0.015267   \n",
       "YDL241W            0.000000               0.000000        0.000000   \n",
       "YDL240W            0.037740               0.019418        0.015267   \n",
       "\n",
       "         WT(ho)_Urea  WT(ho)_YPD  WT(ho)_YPDDiauxic  WT(ho)_YPDRapa  ...  \\\n",
       "YDL248W     0.000000    0.000000           0.003180        0.002144  ...   \n",
       "YDL244W     0.006780    0.001024           0.028259        0.022282  ...   \n",
       "YDL243C     0.000000    0.030275           0.111393        0.105790  ...   \n",
       "YDL241W     0.000000    0.013232           0.000000        0.009610  ...   \n",
       "YDL240W     0.020203    0.004090           0.003180        0.018076  ...   \n",
       "\n",
       "         stp2_CStarve  stp2_Glutamine  stp2_MinimalEtOH  stp2_MinimalGlucose  \\\n",
       "YDL248W      0.000000        0.000000          0.000000             0.004040   \n",
       "YDL244W      0.017778        0.000000          0.035091             0.012073   \n",
       "YDL243C      0.043867        0.030583          0.194156             0.035789   \n",
       "YDL241W      0.000000        0.006192          0.000000             0.000000   \n",
       "YDL240W      0.000000        0.012346          0.052186             0.027946   \n",
       "\n",
       "         stp2_Proline  stp2_Urea  stp2_YPD  stp2_YPDDiauxic  stp2_YPDRapa  \\\n",
       "YDL248W      0.009852   0.000000  0.000000         0.006494      0.003743   \n",
       "YDL244W      0.009852   0.000000  0.001278         0.012945      0.024693   \n",
       "YDL243C      0.019608   0.017242  0.020254         0.090263      0.113329   \n",
       "YDL241W      0.000000   0.017242  0.006374         0.000000      0.004988   \n",
       "YDL240W      0.000000   0.008658  0.005102         0.009725      0.009950   \n",
       "\n",
       "         stp2_YPEtOH  \n",
       "YDL248W     0.000000  \n",
       "YDL244W     0.061875  \n",
       "YDL243C     0.068520  \n",
       "YDL241W     0.000000  \n",
       "YDL240W     0.027974  \n",
       "\n",
       "[5 rows x 132 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cleaned and pseudobulked data\n",
    "params = {\n",
    "    'nonzero' : {'yeast_expression' : '../data/processed/pseudobulk_nonzero_logcounts.tsv',\\\n",
    "               'output_folder' : '../results/pseudobulk_nonzero/'},\\\n",
    "    'perc02' : {'yeast_expression': '../data/processed/pseudobulk_perc02_logcounts.tsv',\\\n",
    "              'output_folder' : '../results/pseudobulk_perc02/'}\n",
    "}\n",
    "params\n",
    "\n",
    "choice = 'perc02'\n",
    "\n",
    "yeast_expression = params[choice]['yeast_expression']\n",
    "output_folder = params[choice]['output_folder']\n",
    "\n",
    "df = pd.read_csv(yeast_expression, sep = '\\t', index_col = 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient = '../data/processed/sweet_patient_%s.txt' %choice\n",
    "gene = '../data/processed/sweet_gene_%s.txt' %choice\n",
    "\n",
    "ppp = df.columns.tolist()\n",
    "pd.DataFrame(ppp).to_csv(patient, index = False, header = False, sep = '\\t')\n",
    "\n",
    "ggg = df.index.tolist()\n",
    "pd.DataFrame(ggg).to_csv(gene, index = False, header = False, sep = '\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132, 5804)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ppp), len(ggg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../data/processed/pseudobulk_nonzero_logcounts.tsv'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yeast_expression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute the weights\n",
    "These steps can be done outside of this notebook. \n",
    "\n",
    "```bash\n",
    "python3 ../src/sweet/1.SWEET_sample_weight_calculating.py -f '../data/processed_data/GSE125162_all_pseudobulk_logcounts.txt' -s ../data/networks/sweet/GSE125162_all_pseudobulk_logcounts_weight.txt\n",
    "```\n",
    "\n",
    "```bash\n",
    "python3 ../src/sweet/1.SWEET_sample_weight_calculating.py -f '../data/processed/pseudobulk_nonzero_logcounts.tsv' -s '../data/processed/sweet_weights_pseudobulk_nonzero_logcounts.tsv'\n",
    "```\n",
    "\n",
    "```bash\n",
    "python3 ../src/sweet/1.SWEET_sample_weight_calculating.py -f '../data/processed/pseudobulk_perc02_logcounts.tsv' -s '../data/processed/sweet_weights_pseudobulk_perc02_logcounts.tsv'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>sample_weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT(ho)_AmmoniumSulfate</td>\n",
       "      <td>11.147299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WT(ho)_CStarve</td>\n",
       "      <td>3.725125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WT(ho)_Glutamine</td>\n",
       "      <td>13.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WT(ho)_MinimalEtOH</td>\n",
       "      <td>10.499402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WT(ho)_MinimalGlucose</td>\n",
       "      <td>12.071692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>stp2_Urea</td>\n",
       "      <td>10.134015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>stp2_YPD</td>\n",
       "      <td>8.592073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>stp2_YPDDiauxic</td>\n",
       "      <td>1.302940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>stp2_YPDRapa</td>\n",
       "      <td>11.482504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>stp2_YPEtOH</td>\n",
       "      <td>11.547431</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    patient  sample_weight\n",
       "0    WT(ho)_AmmoniumSulfate      11.147299\n",
       "1            WT(ho)_CStarve       3.725125\n",
       "2          WT(ho)_Glutamine      13.200000\n",
       "3        WT(ho)_MinimalEtOH      10.499402\n",
       "4     WT(ho)_MinimalGlucose      12.071692\n",
       "..                      ...            ...\n",
       "127               stp2_Urea      10.134015\n",
       "128                stp2_YPD       8.592073\n",
       "129         stp2_YPDDiauxic       1.302940\n",
       "130            stp2_YPDRapa      11.482504\n",
       "131             stp2_YPEtOH      11.547431\n",
       "\n",
       "[132 rows x 2 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights_nonzero = pd.read_csv('../data/processed/sweet_weights_pseudobulk_nonzero_logcounts.tsv', sep  = '\\t')\n",
    "weights_nonzero"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>sample_weight</th>\n",
       "      <th>condition</th>\n",
       "      <th>genetic</th>\n",
       "      <th>sample_idx</th>\n",
       "      <th>idx</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WT(ho)_Glutamine</td>\n",
       "      <td>13.200000</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>WT(ho)</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>gln3_Glutamine</td>\n",
       "      <td>13.181181</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>gln3</td>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>rtg1_Glutamine</td>\n",
       "      <td>13.009167</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>rtg1</td>\n",
       "      <td>90</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dal82_Glutamine</td>\n",
       "      <td>12.980633</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>dal82</td>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dal80_Glutamine</td>\n",
       "      <td>12.974304</td>\n",
       "      <td>Glutamine</td>\n",
       "      <td>dal80</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>gat1_YPDDiauxic</td>\n",
       "      <td>1.549727</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>gat1</td>\n",
       "      <td>52</td>\n",
       "      <td>127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>stp2_YPDDiauxic</td>\n",
       "      <td>1.294735</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>stp2</td>\n",
       "      <td>129</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>gcn4_YPDDiauxic</td>\n",
       "      <td>1.275940</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>gcn4</td>\n",
       "      <td>63</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>dal81_YPDDiauxic</td>\n",
       "      <td>1.192588</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>dal81</td>\n",
       "      <td>30</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>stp1_YPDDiauxic</td>\n",
       "      <td>0.489317</td>\n",
       "      <td>YPDDiauxic</td>\n",
       "      <td>stp1</td>\n",
       "      <td>118</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              patient  sample_weight   condition genetic  sample_idx  idx\n",
       "0    WT(ho)_Glutamine      13.200000   Glutamine  WT(ho)           2    0\n",
       "1      gln3_Glutamine      13.181181   Glutamine    gln3          68    1\n",
       "2      rtg1_Glutamine      13.009167   Glutamine    rtg1          90    2\n",
       "3     dal82_Glutamine      12.980633   Glutamine   dal82          35    3\n",
       "4     dal80_Glutamine      12.974304   Glutamine   dal80          13    4\n",
       "..                ...            ...         ...     ...         ...  ...\n",
       "127   gat1_YPDDiauxic       1.549727  YPDDiauxic    gat1          52  127\n",
       "128   stp2_YPDDiauxic       1.294735  YPDDiauxic    stp2         129  128\n",
       "129   gcn4_YPDDiauxic       1.275940  YPDDiauxic    gcn4          63  129\n",
       "130  dal81_YPDDiauxic       1.192588  YPDDiauxic   dal81          30  130\n",
       "131   stp1_YPDDiauxic       0.489317  YPDDiauxic    stp1         118  131\n",
       "\n",
       "[132 rows x 6 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weights_nonzero = pd.read_csv('../data/processed/sweet_weights_pseudobulk_nonzero_logcounts.tsv', sep  = '\\t')\n",
    "weights_perc02 = pd.read_csv('../data/processed/sweet_weights_pseudobulk_perc02_logcounts.tsv', sep = '\\t')\n",
    "\n",
    "weights_nonzero['condition'] = weights_nonzero['patient'].str.split('_').str[1]\n",
    "weights_nonzero['genetic'] = weights_nonzero['patient'].str.split('_').str[0]\n",
    "\n",
    "weights_perc02['condition'] = weights_perc02['patient'].str.split('_').str[1]\n",
    "weights_perc02['genetic'] = weights_perc02['patient'].str.split('_').str[0]\n",
    "\n",
    "\n",
    "weights_nonzero['sample_idx'] = weights_nonzero.index.tolist()\n",
    "weights_perc02['sample_idx'] = weights_perc02.index.tolist()\n",
    "\n",
    "# Plot the weights colored by condition and genetics\n",
    "weights_nonzero = weights_nonzero.sort_values(by = 'sample_weight', ascending = False).reset_index(drop = True)\n",
    "weights_perc02 = weights_perc02.sort_values(by = 'sample_weight', ascending = False).reset_index(drop = True)\n",
    "\n",
    "weights_nonzero['idx'] = weights_nonzero.index.tolist()\n",
    "weights_perc02['idx'] = weights_perc02.index.tolist()\n",
    "\n",
    "weights_perc02"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SignificanceResult(statistic=0.9944482997918112, pvalue=3.5746806931383214e-64)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "allw = weights_perc02.merge(weights_nonzero, on = 'patient', suffixes = ['_perc02', '_nonzero'])\n",
    "allw\n",
    "\n",
    "import scipy.stats as stats\n",
    "stats.kendalltau(allw['idx_nonzero'], allw['idx_perc02'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(1,2, figsize = (10,5))\n",
    "sns.scatterplot(x = allw['idx_nonzero'], y = allw['idx_perc02'], ax = ax[0], hue = allw['condition_nonzero'], palette = 'Set1')\n",
    "sns.scatterplot(x = allw['idx_nonzero'], y = allw['idx_perc02'], ax = ax[1], hue = allw['genetic_nonzero'], palette = 'Set2')\n",
    "ax[0].set_ylabel('Order nonzero')\n",
    "ax[0].set_xlabel('Order perc02')\n",
    "ax[1].set_ylabel('Order nonzero')\n",
    "ax[1].set_xlabel('Order perc02')\n",
    "ax[0].annotate('Kendall Tau = %.2f' %stats.kendalltau(allw['idx_nonzero'], allw['idx_perc02'])[0], xy = (0.5,0.1), xycoords = 'axes fraction')\n",
    "f.suptitle('Order of samples in the weights\\n Colored by media/genetics')\n",
    "f.savefig('../results/sweet_weights_nonzero_vs_perc02.pdf')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x2000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "\n",
    "\n",
    "sns.set_context('paper')\n",
    "sns.set_style('whitegrid')\n",
    "f,ax = plt.subplots(1,2,figsize = (10,20))\n",
    "sns.scatterplot(x = 'sample_weight', y= 'patient', hue = 'condition' , data = weights_nonzero, ax = ax[0], palette = 'Set1')\n",
    "ax[0].set_ylabel('SWEET weight')\n",
    "ax[0].set_xlabel('samples')\n",
    "ax[0].set_title('Nonzero')\n",
    "\n",
    "sns.scatterplot(x = 'sample_weight', y= 'patient', hue = 'condition' , data = weights_perc02, ax = ax[1], palette = 'Set1')\n",
    "ax[1].set_ylabel('SWEET weight')\n",
    "ax[1].set_xlabel('samples')\n",
    "ax[1].set_title('Perc02')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "f.savefig('../data/processed/sweet_weights.pdf')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Network inference\n",
    "\n",
    "``` bash\n",
    "python3 ../src/sweet/2.SWEET_edge_score_calculating.py -f '../data/processed_data/GSE125162_all_pseudobulk_logcounts.txt' \\\n",
    "    -w '../data/networks/sweet/GSE125162_all_pseudobulk_logcounts_weight.txt' \\\n",
    "        -p '../data/processed_data/sweet_patient.txt' \\\n",
    "        -g '../data/processed_data/sweet_gene.txt' \\\n",
    "        -s '../data/networks/sweet/'\n",
    "```\n",
    "\n",
    "\n",
    "``` bash\n",
    "python3 ../src/sweet/2.SWEET_edge_score_calculating.py -f '../data/processed/pseudobulk_nonzero_logcounts.tsv' \\\n",
    "    -w '../data/processed/sweet_weights_pseudobulk_nonzero_logcounts.tsv' \\\n",
    "        -p '../data/processed/sweet_patient_nonzero.txt' \\\n",
    "        -g '../data/processed/sweet_gene_nonzero.txt' \\\n",
    "        -s '../results/pseudobulk_nonzero/sweet/networks/'\n",
    "```\n",
    "\n",
    "\n",
    "``` bash\n",
    "python3 ../src/sweet/2.SWEET_edge_score_calculating.py -f '../data/processed/pseudobulk_perc02_logcounts.tsv' \\\n",
    "    -w '../data/processed/sweet_weights_pseudobulk_perc02_logcounts.tsv' \\\n",
    "        -p '../data/processed/sweet_patient_perc02.txt' \\\n",
    "        -g '../data/processed/sweet_gene_perc02.txt' \\\n",
    "        -s '../results/pseudobulk_perc02/sweet/networks/'\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YLR437C.A</td>\n",
       "      <td>YKL097C</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YHR070C.A</td>\n",
       "      <td>YCL007C</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL172C</td>\n",
       "      <td>YGR273C</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YBL075C</td>\n",
       "      <td>-0.900916</td>\n",
       "      <td>-0.925252</td>\n",
       "      <td>-0.904306</td>\n",
       "      <td>-0.894656</td>\n",
       "      <td>-0.904517</td>\n",
       "      <td>-0.904471</td>\n",
       "      <td>-0.902205</td>\n",
       "      <td>-0.898097</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.903776</td>\n",
       "      <td>-0.903657</td>\n",
       "      <td>-0.903295</td>\n",
       "      <td>-0.904727</td>\n",
       "      <td>-0.903859</td>\n",
       "      <td>-0.903421</td>\n",
       "      <td>-0.903968</td>\n",
       "      <td>-0.901682</td>\n",
       "      <td>-0.904262</td>\n",
       "      <td>-0.902266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>YDR524C.B</td>\n",
       "      <td>YMR118C</td>\n",
       "      <td>-0.911044</td>\n",
       "      <td>-0.913116</td>\n",
       "      <td>-0.905269</td>\n",
       "      <td>-0.898125</td>\n",
       "      <td>-0.910290</td>\n",
       "      <td>-0.907408</td>\n",
       "      <td>-0.906107</td>\n",
       "      <td>-0.907003</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.911116</td>\n",
       "      <td>-0.906901</td>\n",
       "      <td>-0.910568</td>\n",
       "      <td>-0.901661</td>\n",
       "      <td>-0.911393</td>\n",
       "      <td>-0.910903</td>\n",
       "      <td>-0.911329</td>\n",
       "      <td>-0.908027</td>\n",
       "      <td>-0.910956</td>\n",
       "      <td>-0.911460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>YLR044C</td>\n",
       "      <td>YMR175W</td>\n",
       "      <td>-0.911895</td>\n",
       "      <td>-0.918413</td>\n",
       "      <td>-0.919989</td>\n",
       "      <td>-0.911141</td>\n",
       "      <td>-0.911669</td>\n",
       "      <td>-0.919478</td>\n",
       "      <td>-0.921646</td>\n",
       "      <td>-0.915302</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.919619</td>\n",
       "      <td>-0.900025</td>\n",
       "      <td>-0.916977</td>\n",
       "      <td>-0.917661</td>\n",
       "      <td>-0.919999</td>\n",
       "      <td>-0.917696</td>\n",
       "      <td>-0.916434</td>\n",
       "      <td>-0.917815</td>\n",
       "      <td>-0.915195</td>\n",
       "      <td>-0.920165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YHR139C</td>\n",
       "      <td>-0.918214</td>\n",
       "      <td>-0.937680</td>\n",
       "      <td>-0.917121</td>\n",
       "      <td>-0.913532</td>\n",
       "      <td>-0.917192</td>\n",
       "      <td>-0.916967</td>\n",
       "      <td>-0.917913</td>\n",
       "      <td>-0.909502</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.916583</td>\n",
       "      <td>-0.918202</td>\n",
       "      <td>-0.917132</td>\n",
       "      <td>-0.915102</td>\n",
       "      <td>-0.917174</td>\n",
       "      <td>-0.918715</td>\n",
       "      <td>-0.917356</td>\n",
       "      <td>-0.916954</td>\n",
       "      <td>-0.916532</td>\n",
       "      <td>-0.914922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YMR118C</td>\n",
       "      <td>-0.954437</td>\n",
       "      <td>-0.965458</td>\n",
       "      <td>-0.956239</td>\n",
       "      <td>-0.942384</td>\n",
       "      <td>-0.956242</td>\n",
       "      <td>-0.955953</td>\n",
       "      <td>-0.953498</td>\n",
       "      <td>-0.951076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.955654</td>\n",
       "      <td>-0.956424</td>\n",
       "      <td>-0.956487</td>\n",
       "      <td>-0.954672</td>\n",
       "      <td>-0.955917</td>\n",
       "      <td>-0.956046</td>\n",
       "      <td>-0.956560</td>\n",
       "      <td>-0.955413</td>\n",
       "      <td>-0.955992</td>\n",
       "      <td>-0.954399</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         gene1      gene2  rtg3_AmmoniumSulfate  gcn4_CStarve  \\\n",
       "0    YBL008W.A  YKL106C.A              1.000000      1.000000   \n",
       "1    YOR161C.C  YHR032C.A              1.000000      1.000000   \n",
       "2    YLR437C.A    YKL097C              1.000000      1.000000   \n",
       "3    YHR070C.A    YCL007C              1.000000      1.000000   \n",
       "4      YDL172C    YGR273C              1.000000      1.000000   \n",
       "..         ...        ...                   ...           ...   \n",
       "995    YLR110C    YBL075C             -0.900916     -0.925252   \n",
       "996  YDR524C.B    YMR118C             -0.911044     -0.913116   \n",
       "997    YLR044C    YMR175W             -0.911895     -0.918413   \n",
       "998    YLR110C    YHR139C             -0.918214     -0.937680   \n",
       "999    YLR110C    YMR118C             -0.954437     -0.965458   \n",
       "\n",
       "     gat1_AmmoniumSulfate  gat1_MinimalEtOH  dal82_YPEtOH  gzf3_Urea  \\\n",
       "0                1.000000          1.000000      1.000000   1.000000   \n",
       "1                1.000000          1.000000      1.000000   1.000000   \n",
       "2                1.000000          1.000000      1.000000   1.000000   \n",
       "3                1.000000          1.000000      1.000000   1.000000   \n",
       "4                1.000000          1.000000      1.000000   1.000000   \n",
       "..                    ...               ...           ...        ...   \n",
       "995             -0.904306         -0.894656     -0.904517  -0.904471   \n",
       "996             -0.905269         -0.898125     -0.910290  -0.907408   \n",
       "997             -0.919989         -0.911141     -0.911669  -0.919478   \n",
       "998             -0.917121         -0.913532     -0.917192  -0.916967   \n",
       "999             -0.956239         -0.942384     -0.956242  -0.955953   \n",
       "\n",
       "     dal81_MinimalEtOH  stp1_MinimalGlucose  ...  WT(ho)_YPDRapa  \\\n",
       "0             1.000000             1.000000  ...        1.000000   \n",
       "1             1.000000             1.000000  ...        1.000000   \n",
       "2             1.000000             1.000000  ...        1.000000   \n",
       "3             1.000000             1.000000  ...        1.000000   \n",
       "4             1.000000             1.000000  ...        1.000000   \n",
       "..                 ...                  ...  ...             ...   \n",
       "995          -0.902205            -0.898097  ...       -0.903776   \n",
       "996          -0.906107            -0.907003  ...       -0.911116   \n",
       "997          -0.921646            -0.915302  ...       -0.919619   \n",
       "998          -0.917913            -0.909502  ...       -0.916583   \n",
       "999          -0.953498            -0.951076  ...       -0.955654   \n",
       "\n",
       "     dal81_YPEtOH  gzf3_Glutamine  gat1_Urea  rtg3_YPDRapa  \\\n",
       "0        1.000000        1.000000   1.000000      1.000000   \n",
       "1        1.000000        1.000000   1.000000      1.000000   \n",
       "2        1.000000        1.000000   1.000000      1.000000   \n",
       "3        1.000000        1.000000   1.000000      1.000000   \n",
       "4        1.000000        1.000000   1.000000      1.000000   \n",
       "..            ...             ...        ...           ...   \n",
       "995     -0.903657       -0.903295  -0.904727     -0.903859   \n",
       "996     -0.906901       -0.910568  -0.901661     -0.911393   \n",
       "997     -0.900025       -0.916977  -0.917661     -0.919999   \n",
       "998     -0.918202       -0.917132  -0.915102     -0.917174   \n",
       "999     -0.956424       -0.956487  -0.954672     -0.955917   \n",
       "\n",
       "     gzf3_AmmoniumSulfate  stp2_Glutamine  stp1_YPDDiauxic  dal82_Urea  \\\n",
       "0                1.000000        1.000000         1.000000    1.000000   \n",
       "1                1.000000        1.000000         1.000000    1.000000   \n",
       "2                1.000000        1.000000         1.000000    1.000000   \n",
       "3                1.000000        1.000000         1.000000    1.000000   \n",
       "4                1.000000        1.000000         1.000000    1.000000   \n",
       "..                    ...             ...              ...         ...   \n",
       "995             -0.903421       -0.903968        -0.901682   -0.904262   \n",
       "996             -0.910903       -0.911329        -0.908027   -0.910956   \n",
       "997             -0.917696       -0.916434        -0.917815   -0.915195   \n",
       "998             -0.918715       -0.917356        -0.916954   -0.916532   \n",
       "999             -0.956046       -0.956560        -0.955413   -0.955992   \n",
       "\n",
       "     stp2_YPDRapa  \n",
       "0        1.000000  \n",
       "1        1.000000  \n",
       "2        1.000000  \n",
       "3        1.000000  \n",
       "4        1.000000  \n",
       "..            ...  \n",
       "995     -0.902266  \n",
       "996     -0.911460  \n",
       "997     -0.920165  \n",
       "998     -0.914922  \n",
       "999     -0.954399  \n",
       "\n",
       "[1000 rows x 134 columns]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open ('../../GSE125162_all_pseudobulk_logcounts_sweet_networks_top1k.txt', 'r') as f:\n",
    "    old_sweet = pd.read_csv(f, sep = '\\t')\n",
    "old_sweet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>raw_edge_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL247W</td>\n",
       "      <td>0.002127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL246C</td>\n",
       "      <td>-0.041748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL245C</td>\n",
       "      <td>-0.061379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL244W</td>\n",
       "      <td>0.184680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL243C</td>\n",
       "      <td>0.053967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251935</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>0.080211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251936</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>0.049207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251937</th>\n",
       "      <td>RPM1</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>-0.063856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251938</th>\n",
       "      <td>RPM1</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>-0.002815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251939</th>\n",
       "      <td>KANMX</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>0.714482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21251940 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene1    gene2  raw_edge_score\n",
       "0         YDL248W  YDL247W        0.002127\n",
       "1         YDL248W  YDL246C       -0.041748\n",
       "2         YDL248W  YDL245C       -0.061379\n",
       "3         YDL248W  YDL244W        0.184680\n",
       "4         YDL248W  YDL243C        0.053967\n",
       "...           ...      ...             ...\n",
       "21251935    Q0275    KANMX        0.080211\n",
       "21251936    Q0275    NATMX        0.049207\n",
       "21251937     RPM1    KANMX       -0.063856\n",
       "21251938     RPM1    NATMX       -0.002815\n",
       "21251939    KANMX    NATMX        0.714482\n",
       "\n",
       "[21251940 rows x 3 columns]"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sweet_net = pd.read_csv('../results/pseudobulk_nonzero/sweet/networks/WT(ho)_AmmoniumSulfate.txt', sep = '\\t')\n",
    "sweet_net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>WT(ho)_AmmoniumSulfate</th>\n",
       "      <th>raw_edge_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62554</th>\n",
       "      <td>YBL008W.A</td>\n",
       "      <td>YKL106C.A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17892595</th>\n",
       "      <td>YOR161C.C</td>\n",
       "      <td>YHR032C.A</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13321069</th>\n",
       "      <td>YLR437C.A</td>\n",
       "      <td>YKL097C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11398964</th>\n",
       "      <td>YLL066C</td>\n",
       "      <td>snR24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2424884</th>\n",
       "      <td>YDL172C</td>\n",
       "      <td>YGR273C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251935</th>\n",
       "      <td>snR9</td>\n",
       "      <td>snR82</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.547619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251936</th>\n",
       "      <td>snR9</td>\n",
       "      <td>snR83</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.030368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251937</th>\n",
       "      <td>snR9</td>\n",
       "      <td>snR85</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.039323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251938</th>\n",
       "      <td>snR9</td>\n",
       "      <td>snR86</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.405971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21251939</th>\n",
       "      <td>snR9</td>\n",
       "      <td>snR87</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.563426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21251940 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              gene1      gene2  WT(ho)_AmmoniumSulfate  raw_edge_score\n",
       "62554     YBL008W.A  YKL106C.A                     1.0        1.000000\n",
       "17892595  YOR161C.C  YHR032C.A                     1.0        1.000000\n",
       "13321069  YLR437C.A    YKL097C                     1.0        1.000000\n",
       "11398964    YLL066C      snR24                     1.0        1.000000\n",
       "2424884     YDL172C    YGR273C                     1.0        1.000000\n",
       "...             ...        ...                     ...             ...\n",
       "21251935       snR9      snR82                     NaN        0.547619\n",
       "21251936       snR9      snR83                     NaN        0.030368\n",
       "21251937       snR9      snR85                     NaN        0.039323\n",
       "21251938       snR9      snR86                     NaN        0.405971\n",
       "21251939       snR9      snR87                     NaN       -0.563426\n",
       "\n",
       "[21251940 rows x 4 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(old_sweet.loc[:,['gene1','gene2','WT(ho)_AmmoniumSulfate']]).merge(sweet_net, on = ['gene1','gene2'], how = 'outer').sort_values(by = 'WT(ho)_AmmoniumSulfate', ascending = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get top1k network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# compute mean and std of all networks accessing each network separately\n",
    "def compose_std(x, m):\n",
    "    return(np.sum((x - m)**2))\n",
    "    \n",
    "\n",
    "def compute_composed_mean_and_std(nets_filename_list):\n",
    "    means = []\n",
    "    sss = []\n",
    "    ns = []\n",
    "\n",
    "    for i,net in enumerate(nets_filename_list):\n",
    "        with open(net,'r') as f:\n",
    "            net = pd.read_csv(f, sep = '\\t')\n",
    "        net['raw_edge_score'] = net['raw_edge_score'].astype(float)\n",
    "        ns.append(len(net))\n",
    "        means.append(net['raw_edge_score'].mean())\n",
    "        #stds.append(net['raw_edge_score'].std())\n",
    "    \n",
    "    means = np.array(means)\n",
    "    ns = np.array(ns)\n",
    "    mean = np.sum(means*ns)/np.sum(ns)\n",
    "    \n",
    "    for i,net in enumerate(nets_filename_list):\n",
    "        with open(net,'r') as f:\n",
    "            net = pd.read_csv(f, sep = '\\t')\n",
    "        net['raw_edge_score'] = net['raw_edge_score'].astype(float)\n",
    "        sss.append(compose_std(net['raw_edge_score'].values, mean))\n",
    "    sss = np.array(sss)\n",
    "        \n",
    "    std2 = np.sqrt(np.sum(sss)/np.sum(ns))\n",
    "    \n",
    "    \n",
    "    \n",
    "    return mean, std2\n",
    "\n",
    "\n",
    "def compute_rowise_mean(nets_filename_list, verbose = 5):\n",
    "    means = []\n",
    "    ns = []\n",
    "\n",
    "    for i,net in enumerate(nets_filename_list):\n",
    "        if (i%verbose==0):\n",
    "            print('Reading network %d' %i)\n",
    "        with open(net,'r') as f:\n",
    "            net = pd.read_csv(f, sep = '\\t')\n",
    "        if i==0:\n",
    "            edges = np.zeros(len(net))\n",
    "        edges += net['raw_edge_score'].astype(float)\n",
    "\n",
    "    \n",
    "    edges = edges/len(nets_filename_list)\n",
    "    mean_net = pd.DataFrame({'gene1':net['gene1'], 'gene2':net['gene2'], 'mean_edge':edges})\n",
    "    return(mean_net)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../results/pseudobulk_perc02/sweet/networks/rtg1_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_MinimalEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_MinimalGlucose.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal82_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg3_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_YPD.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gcn4_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal80_CStarve.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gzf3_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_Proline.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gat1_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_Urea.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_YPEtOH.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/WT(ho)_Glutamine.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp2_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/rtg1_AmmoniumSulfate.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/gln3_YPDDiauxic.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/dal81_YPDRapa.txt',\n",
       " '../results/pseudobulk_perc02/sweet/networks/stp1_YPEtOH.txt']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nets_fn = glob.glob(output_folder + 'sweet/networks/*')\n",
    "nets_fn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the top1k \n",
    "mean_net = compute_rowise_mean(nets_fn, verbose = 10)\n",
    "mean_net = mean_net.sort_values(by = ['mean_edge'], ascending = False)\n",
    "# Save the mean network\n",
    "mean_net.to_csv(output_folder + 'sweet/mean_network.txt', sep = '\\t', index = False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>mean_edge</th>\n",
       "      <th>edge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YGR034W</td>\n",
       "      <td>YLL045C</td>\n",
       "      <td>0.994845</td>\n",
       "      <td>YGR034W-YLL045C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDR064W</td>\n",
       "      <td>YJR123W</td>\n",
       "      <td>0.994838</td>\n",
       "      <td>YDR064W-YJR123W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YGL030W</td>\n",
       "      <td>YJR123W</td>\n",
       "      <td>0.994532</td>\n",
       "      <td>YGL030W-YJR123W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDR064W</td>\n",
       "      <td>YGL030W</td>\n",
       "      <td>0.994301</td>\n",
       "      <td>YDR064W-YGL030W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YOL120C</td>\n",
       "      <td>YJR123W</td>\n",
       "      <td>0.994270</td>\n",
       "      <td>YOL120C-YJR123W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840301</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YBL075C</td>\n",
       "      <td>-0.900170</td>\n",
       "      <td>YLR110C-YBL075C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840302</th>\n",
       "      <td>YDR524C.B</td>\n",
       "      <td>YMR118C</td>\n",
       "      <td>-0.908380</td>\n",
       "      <td>YDR524C.B-YMR118C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840303</th>\n",
       "      <td>YLR044C</td>\n",
       "      <td>YMR175W</td>\n",
       "      <td>-0.913207</td>\n",
       "      <td>YLR044C-YMR175W</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840304</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YHR139C</td>\n",
       "      <td>-0.916708</td>\n",
       "      <td>YLR110C-YHR139C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840305</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YMR118C</td>\n",
       "      <td>-0.954002</td>\n",
       "      <td>YLR110C-YMR118C</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              gene1    gene2  mean_edge               edge\n",
       "0           YGR034W  YLL045C   0.994845    YGR034W-YLL045C\n",
       "1           YDR064W  YJR123W   0.994838    YDR064W-YJR123W\n",
       "2           YGL030W  YJR123W   0.994532    YGL030W-YJR123W\n",
       "3           YDR064W  YGL030W   0.994301    YDR064W-YGL030W\n",
       "4           YOL120C  YJR123W   0.994270    YOL120C-YJR123W\n",
       "...             ...      ...        ...                ...\n",
       "16840301    YLR110C  YBL075C  -0.900170    YLR110C-YBL075C\n",
       "16840302  YDR524C.B  YMR118C  -0.908380  YDR524C.B-YMR118C\n",
       "16840303    YLR044C  YMR175W  -0.913207    YLR044C-YMR175W\n",
       "16840304    YLR110C  YHR139C  -0.916708    YLR110C-YHR139C\n",
       "16840305    YLR110C  YMR118C  -0.954002    YLR110C-YMR118C\n",
       "\n",
       "[1000 rows x 4 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_net = pd.read_csv(output_folder + 'sweet/mean_network.txt', sep = '\\t')\n",
    "mean_net['edge'] = mean_net['gene1'] + '-' + mean_net['gene2']\n",
    "mean_net_top1k = mean_net.loc[np.r_[mean_net.index.tolist()[:500],mean_net.index.tolist()[-500:]],:]\n",
    "mean_net_top1k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10504521,  6377735, 10332340,  8260319,  8477625,   813794,\n",
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       "       15786153,  1289611,  3757552,  7258023, 14774398,  2347764,\n",
       "       16512584,  4952398,  4694004,  9438977, 16545351, 12439044,\n",
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       "         996696, 15134796, 16398886, 14750334,  6198188,  3076952,\n",
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       "        7718201,  9178794, 10109082,  4128760,  2881769, 14322795,\n",
       "        1254582,  1314112,  3699461,  8370969,   975442, 16068641,\n",
       "         632170,  7923092,  8504899,  6463150, 16144946,  1842818,\n",
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       "        8908940, 16173222,  7237290, 11853219,  1151950,   138860,\n",
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       "       15940253,  9053867,  4682436,  8702157,  2341802,  8203438,\n",
       "       12429680,  2306449,  4076992, 15578726, 16052281,   244782,\n",
       "       10168027,  9218227, 14134934,  8603419,  5154340, 14181605,\n",
       "       10106894, 12899936,  3110261,  3163591,  9279769,  5552893,\n",
       "        4683360,  3519482,  6057454,  2972027, 13476870, 11700560,\n",
       "        8837445,  1030359, 16107568, 12541231,  4486170, 13243891,\n",
       "         448690,  4678446,  4832431,  8750534,  5162394, 15095338,\n",
       "        3939902,  4779663, 12069261,  6005991,  2932859,  9971191,\n",
       "         618043, 13611021, 16401492, 14244620, 16460519, 15447008,\n",
       "       15712951, 15680141, 12678722,  5492496,   929658,  8520059,\n",
       "       15738188, 12633144, 14733215, 15668661, 10192211,  6869898,\n",
       "       11414233, 13022909,  4507053,  1821866,  8268308,  4351760,\n",
       "        1668902,  4602429,  1846597, 13593593,  5797128,  5404421,\n",
       "       16734014,  8801463, 14631685, 16067580,  5062422, 12219282,\n",
       "        1361316, 12791845,  1538092,  1304702,   570269,    25649,\n",
       "       14369288,   411899,  2647113,  8823999, 16529301,   204534,\n",
       "        3574165,  7081403,   148297, 12071880,  6165055, 14876445,\n",
       "       10943331, 16321558, 10589373, 14465285,  7174218,  3382333,\n",
       "        5593417,  4417022, 10474652,  7060307,  5444590, 14210466,\n",
       "       14438335,   412598, 11087659, 11523176, 15305428, 16602250,\n",
       "       10101090, 15875796, 13360970,  4666981, 16381967,  8605399,\n",
       "        7087691, 13560676,   652214, 15937311,  8489757,   341022,\n",
       "       11606157,   760107, 14592229,  7691092, 11770250, 14552486,\n",
       "       16176559, 15436698,  3472944,  9739319,  7499629, 11716810,\n",
       "         558127, 11249062,  6855855, 11544277,  8398194,  2378620,\n",
       "        4284015,  7988603,  7284582, 16203972, 15411514,  2227408,\n",
       "       11741058,  5996000, 11729042, 15478407,  8775633, 12257751,\n",
       "        1751557,   846905,  8856235,  5131643,  7552509,  7504047,\n",
       "        6519036, 16020687,  3167591, 16493179,  8854992, 14362036,\n",
       "        5035521,  3103750,  2278007, 12970499, 13781574,  3765186,\n",
       "       14444118,  4947940, 15205733,  4208724,  2932193,  8765322,\n",
       "       12054771,  8392794,  6021318,  1070115,  9931920,  2083510,\n",
       "        3504768,  8628323, 13128903, 11352459,  4938483, 11224941,\n",
       "       16439508, 10933209,   910346,  8039317, 13542588,  4927953,\n",
       "        3190307,  7298156,  3445646, 10266857, 16099624,  1737209,\n",
       "        9423260, 10407010,  6098324,  6505777, 10919417,  6084312,\n",
       "        4951828,  7620162,  4049401,  6203405,  1325527,  6313098,\n",
       "        3578954,  2715632, 13087587, 14766037, 12355711,  5172639,\n",
       "        5480157,  6788970,  8925492, 13351827,  1454320,  2028697,\n",
       "       14141639, 16759975, 10087385,  7607546,  3242903, 13874658,\n",
       "        7421890, 14936861, 16545584,   262586, 13232514,  1116551,\n",
       "        4579547, 11572239, 11241506,  7470141,  5590058,  1219469,\n",
       "       11051287,  6807691,  7598625,  6256457, 13421951, 13599182,\n",
       "       15923665,  4703212,  6626085,  6632580, 14920822, 14334149,\n",
       "        5654745,  7970267,  5212286,  1530375,  8936045, 14262056,\n",
       "       13061387,  3389386, 12121738,  2600963,  3881409,  9969855,\n",
       "       15618148,  5646006, 13545593,  1811029, 16395865,  9462385,\n",
       "       14884491, 10800641,  4922055, 13663567])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random_index = np.random.choice(mean_net.index, 1000, replace = False)\n",
    "random_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading network 0\n",
      "Reading network 10\n",
      "Reading network 20\n",
      "Reading network 30\n",
      "Reading network 40\n",
      "Reading network 50\n",
      "Reading network 60\n",
      "Reading network 70\n",
      "Reading network 80\n",
      "Reading network 90\n",
      "Reading network 100\n",
      "Reading network 110\n",
      "Reading network 120\n",
      "Reading network 130\n"
     ]
    }
   ],
   "source": [
    "top_1k_edges = mean_net_top1k['edge'].tolist()\n",
    "random_index = np.random.choice(mean_net.index, 1000, replace = False)\n",
    "\n",
    "for i,netfn in enumerate(nets_fn):\n",
    "    if (i%10)==0:\n",
    "        print('Reading network %d' %i)\n",
    "    with open(netfn,'r') as f:\n",
    "        net = pd.read_csv(f, sep = '\\t')\n",
    "        net_name = netfn.split('/')[-1].split('.')[0]\n",
    "        net.columns = ['gene1','gene2',net_name]\n",
    "        net['edge'] = net['gene1'] + '-' + net['gene2']\n",
    "        net.set_index('edge', inplace = True)\n",
    "    if i==0:\n",
    "        net_top1k = net.loc[top_1k_edges,:]\n",
    "        net_random1k = net.iloc[random_index,:]\n",
    "    else:\n",
    "        net_top1k = pd.concat([net_top1k, net.loc[top_1k_edges,net_name]], axis = 1)\n",
    "        net_random1k = pd.concat([net_random1k, net.iloc[random_index,:][net_name]], axis = 1)\n",
    "\n",
    "# Save top1k network\n",
    "net_top1k.reset_index(drop=True, inplace=True)\n",
    "net_top1k.to_hdf(output_folder + 'sweet_top1k.h5', key = 'sweet')\n",
    "\n",
    "# Save top1k network\n",
    "net_random1k.reset_index(drop=True, inplace=True)\n",
    "net_random1k.to_hdf(output_folder + 'sweet_random1k.h5', key = 'sweet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get the sparse network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here is the code to compute the mean and std of the composed network\n",
    "# This is quite slow, so I will not run it now\n",
    "nets_fn = glob.glob(output_folder + 'sweet/networks/*')\n",
    "if True:\n",
    "    m,s= compute_composed_mean_and_std(nets_fn)\n",
    "\n",
    "# m, s for 6520 genes\n",
    "#mean: 0.2204959613090935, std: 0.2430662602336294\n",
    "\n",
    "# For 5804 genes\n",
    "#(0.2675395027695422, 0.24521459368054582)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2675395027695422, 0.24521459368054582)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m,s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'perc02'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "choice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# For non-zero filtering\n",
    "if choice=='nonzero':\n",
    "    all_mean = 0.2204959613090935\n",
    "    all_std = 0.2430662602336294\n",
    "elif choice == 'perc02':\n",
    "    all_mean = 0.2675395027695422\n",
    "    all_std = 0.24521459368054582\n",
    "else:\n",
    "    sys.exit('Choice unknown')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sparse_net(net_filename, mean, std, p_th = 0.01):\n",
    "        with open(net_filename,'r') as f:\n",
    "            net = pd.read_csv(f, sep = '\\t')\n",
    "        net['raw_edge_score'] = net['raw_edge_score'].astype(float)\n",
    "        net['zscores'] = (net['raw_edge_score'] - mean)/std\n",
    "        zth = stats.norm.isf(p_th/2)\n",
    "        net = net[net['zscores'].abs()>zth]\n",
    "        return(net.iloc[:,:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'../results/pseudobulk_perc02/'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing network 0\n",
      "Processing network 5\n",
      "Processing network 10\n",
      "Processing network 15\n",
      "Processing network 20\n",
      "Processing network 25\n",
      "Processing network 30\n",
      "Processing network 35\n",
      "Processing network 40\n",
      "Processing network 45\n",
      "Processing network 50\n",
      "Processing network 55\n",
      "Processing network 60\n",
      "Processing network 65\n",
      "Processing network 70\n",
      "Processing network 75\n",
      "Processing network 80\n",
      "Processing network 85\n",
      "Processing network 90\n",
      "Processing network 95\n",
      "Processing network 100\n",
      "Processing network 105\n",
      "Processing network 110\n",
      "Processing network 115\n",
      "Processing network 120\n",
      "Processing network 125\n",
      "Processing network 130\n"
     ]
    }
   ],
   "source": [
    "sparse_sweet = get_sparse_net(nets_fn[0], all_mean, all_std, p_th = 0.01)\n",
    "sparse_sweet.columns = ['gene1','gene2',nets_fn[0].split('/')[-1][:-4]]\n",
    "\n",
    "for i, net_filename in enumerate(nets_fn[1:]):\n",
    "    if i%5==0:\n",
    "        print('Processing network %d' % i)\n",
    "    net = get_sparse_net(net_filename, all_mean, all_std, p_th = 0.01)\n",
    "    net.columns = ['gene1','gene2',net_filename.split('/')[-1][:-4]]\n",
    "    sparse_sweet = pd.merge(sparse_sweet, net, how = 'outer', on = ['gene1','gene2'])\n",
    "sparse_sweet\n",
    "#sparse_sweet.to_csv('../data/networks/GSE125162_all_pseudobulk_logcounts_sweet_networks_sparse.txt', index = False, sep\n",
    "#= '\\t')\n",
    "sparse_sweet.to_hdf(output_folder + 'sweet_sparse001.h5', key = 'sweet')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## LIONESS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_lioness_dataframe(nets_fn):\n",
    "\n",
    "    df_lioness = pd.DataFrame()\n",
    "\n",
    "    for iii,bbb in enumerate(nets_fn):\n",
    "        print(bbb)\n",
    "        if iii>-1:\n",
    "            k = bbb.split('/')[-1][:-3][8:]\n",
    "            temp = pd.read_hdf(bbb, key = 'lioness')\n",
    "                \n",
    "            temp.index = temp.columns\n",
    "            # Upper triangular matrix\n",
    "            temp = temp.where(np.triu(np.ones(temp.shape), k = 1).astype(bool))\n",
    "            # Put in long format\n",
    "            temp = temp.stack().reset_index() \n",
    "            # Rename columns\n",
    "            temp.columns = ['gene1','gene2',k]\n",
    "            \n",
    "            if iii == 0:\n",
    "                df_lioness = temp\n",
    "            else:\n",
    "                df_lioness[k] = temp[k]\n",
    "            \n",
    "\n",
    "    return(df_lioness)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute the networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>WT(ho)_AmmoniumSulfate</th>\n",
       "      <th>WT(ho)_CStarve</th>\n",
       "      <th>WT(ho)_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalEtOH</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>WT(ho)_Proline</th>\n",
       "      <th>WT(ho)_Urea</th>\n",
       "      <th>WT(ho)_YPD</th>\n",
       "      <th>WT(ho)_YPDDiauxic</th>\n",
       "      <th>WT(ho)_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_CStarve</th>\n",
       "      <th>stp2_Glutamine</th>\n",
       "      <th>stp2_MinimalEtOH</th>\n",
       "      <th>stp2_MinimalGlucose</th>\n",
       "      <th>stp2_Proline</th>\n",
       "      <th>stp2_Urea</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>stp2_YPDDiauxic</th>\n",
       "      <th>stp2_YPDRapa</th>\n",
       "      <th>stp2_YPEtOH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL248W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.002144</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.003743</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL244W</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012474</td>\n",
       "      <td>0.006734</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006780</td>\n",
       "      <td>0.001024</td>\n",
       "      <td>0.028259</td>\n",
       "      <td>0.022282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017778</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.035091</td>\n",
       "      <td>0.012073</td>\n",
       "      <td>0.009852</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001278</td>\n",
       "      <td>0.012945</td>\n",
       "      <td>0.024693</td>\n",
       "      <td>0.061875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL243C</th>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.044997</td>\n",
       "      <td>0.020068</td>\n",
       "      <td>0.074108</td>\n",
       "      <td>0.014599</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.030275</td>\n",
       "      <td>0.111393</td>\n",
       "      <td>0.105790</td>\n",
       "      <td>...</td>\n",
       "      <td>0.043867</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.194156</td>\n",
       "      <td>0.035789</td>\n",
       "      <td>0.019608</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.020254</td>\n",
       "      <td>0.090263</td>\n",
       "      <td>0.113329</td>\n",
       "      <td>0.068520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL241W</th>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013232</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009610</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006192</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.017242</td>\n",
       "      <td>0.006374</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004988</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL240W</th>\n",
       "      <td>0.008230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.037740</td>\n",
       "      <td>0.019418</td>\n",
       "      <td>0.015267</td>\n",
       "      <td>0.020203</td>\n",
       "      <td>0.004090</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.018076</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.012346</td>\n",
       "      <td>0.052186</td>\n",
       "      <td>0.027946</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.008658</td>\n",
       "      <td>0.005102</td>\n",
       "      <td>0.009725</td>\n",
       "      <td>0.009950</td>\n",
       "      <td>0.027974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X21S_rRNA</th>\n",
       "      <td>0.145852</td>\n",
       "      <td>0.068714</td>\n",
       "      <td>0.065383</td>\n",
       "      <td>0.900787</td>\n",
       "      <td>0.388990</td>\n",
       "      <td>0.059719</td>\n",
       "      <td>0.121555</td>\n",
       "      <td>0.040166</td>\n",
       "      <td>1.029392</td>\n",
       "      <td>0.099010</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077625</td>\n",
       "      <td>0.030583</td>\n",
       "      <td>0.719580</td>\n",
       "      <td>0.307723</td>\n",
       "      <td>0.038840</td>\n",
       "      <td>0.152908</td>\n",
       "      <td>0.030229</td>\n",
       "      <td>1.046233</td>\n",
       "      <td>0.096446</td>\n",
       "      <td>0.923358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0160</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.013423</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.009509</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004040</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022545</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.014085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0275</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.003180</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.006494</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANMX</th>\n",
       "      <td>2.360386</td>\n",
       "      <td>1.014260</td>\n",
       "      <td>2.011293</td>\n",
       "      <td>1.759183</td>\n",
       "      <td>2.200487</td>\n",
       "      <td>1.268030</td>\n",
       "      <td>2.427748</td>\n",
       "      <td>1.901312</td>\n",
       "      <td>1.486226</td>\n",
       "      <td>1.456467</td>\n",
       "      <td>...</td>\n",
       "      <td>1.001357</td>\n",
       "      <td>2.033365</td>\n",
       "      <td>2.207096</td>\n",
       "      <td>2.372927</td>\n",
       "      <td>1.500772</td>\n",
       "      <td>2.615808</td>\n",
       "      <td>1.996838</td>\n",
       "      <td>1.457781</td>\n",
       "      <td>1.563132</td>\n",
       "      <td>2.406881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATMX</th>\n",
       "      <td>2.588820</td>\n",
       "      <td>1.358721</td>\n",
       "      <td>2.786845</td>\n",
       "      <td>1.978345</td>\n",
       "      <td>2.986127</td>\n",
       "      <td>1.757858</td>\n",
       "      <td>2.659736</td>\n",
       "      <td>3.098374</td>\n",
       "      <td>1.951807</td>\n",
       "      <td>2.841197</td>\n",
       "      <td>...</td>\n",
       "      <td>1.343930</td>\n",
       "      <td>2.772589</td>\n",
       "      <td>2.268068</td>\n",
       "      <td>3.187456</td>\n",
       "      <td>1.753067</td>\n",
       "      <td>2.841871</td>\n",
       "      <td>3.260749</td>\n",
       "      <td>2.014251</td>\n",
       "      <td>3.067180</td>\n",
       "      <td>3.015396</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5804 rows × 132 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           WT(ho)_AmmoniumSulfate  WT(ho)_CStarve  WT(ho)_Glutamine  \\\n",
       "YDL248W                  0.000000        0.000000          0.000000   \n",
       "YDL244W                  0.000000        0.012474          0.006734   \n",
       "YDL243C                  0.008230        0.044997          0.020068   \n",
       "YDL241W                  0.008230        0.000000          0.000000   \n",
       "YDL240W                  0.008230        0.000000          0.013423   \n",
       "...                           ...             ...               ...   \n",
       "X21S_rRNA                0.145852        0.068714          0.065383   \n",
       "Q0160                    0.000000        0.000000          0.013423   \n",
       "Q0275                    0.000000        0.000000          0.000000   \n",
       "KANMX                    2.360386        1.014260          2.011293   \n",
       "NATMX                    2.588820        1.358721          2.786845   \n",
       "\n",
       "           WT(ho)_MinimalEtOH  WT(ho)_MinimalGlucose  WT(ho)_Proline  \\\n",
       "YDL248W              0.000000               0.004890        0.015267   \n",
       "YDL244W              0.000000               0.000000        0.000000   \n",
       "YDL243C              0.074108               0.014599        0.015267   \n",
       "YDL241W              0.000000               0.000000        0.000000   \n",
       "YDL240W              0.037740               0.019418        0.015267   \n",
       "...                       ...                    ...             ...   \n",
       "X21S_rRNA            0.900787               0.388990        0.059719   \n",
       "Q0160                0.000000               0.000000        0.000000   \n",
       "Q0275                0.000000               0.004890        0.000000   \n",
       "KANMX                1.759183               2.200487        1.268030   \n",
       "NATMX                1.978345               2.986127        1.757858   \n",
       "\n",
       "           WT(ho)_Urea  WT(ho)_YPD  WT(ho)_YPDDiauxic  WT(ho)_YPDRapa  ...  \\\n",
       "YDL248W       0.000000    0.000000           0.003180        0.002144  ...   \n",
       "YDL244W       0.006780    0.001024           0.028259        0.022282  ...   \n",
       "YDL243C       0.000000    0.030275           0.111393        0.105790  ...   \n",
       "YDL241W       0.000000    0.013232           0.000000        0.009610  ...   \n",
       "YDL240W       0.020203    0.004090           0.003180        0.018076  ...   \n",
       "...                ...         ...                ...             ...  ...   \n",
       "X21S_rRNA     0.121555    0.040166           1.029392        0.099010  ...   \n",
       "Q0160         0.000000    0.000000           0.009509        0.000000  ...   \n",
       "Q0275         0.000000    0.000000           0.003180        0.000000  ...   \n",
       "KANMX         2.427748    1.901312           1.486226        1.456467  ...   \n",
       "NATMX         2.659736    3.098374           1.951807        2.841197  ...   \n",
       "\n",
       "           stp2_CStarve  stp2_Glutamine  stp2_MinimalEtOH  \\\n",
       "YDL248W        0.000000        0.000000          0.000000   \n",
       "YDL244W        0.017778        0.000000          0.035091   \n",
       "YDL243C        0.043867        0.030583          0.194156   \n",
       "YDL241W        0.000000        0.006192          0.000000   \n",
       "YDL240W        0.000000        0.012346          0.052186   \n",
       "...                 ...             ...               ...   \n",
       "X21S_rRNA      0.077625        0.030583          0.719580   \n",
       "Q0160          0.000000        0.000000          0.000000   \n",
       "Q0275          0.000000        0.000000          0.000000   \n",
       "KANMX          1.001357        2.033365          2.207096   \n",
       "NATMX          1.343930        2.772589          2.268068   \n",
       "\n",
       "           stp2_MinimalGlucose  stp2_Proline  stp2_Urea  stp2_YPD  \\\n",
       "YDL248W               0.004040      0.009852   0.000000  0.000000   \n",
       "YDL244W               0.012073      0.009852   0.000000  0.001278   \n",
       "YDL243C               0.035789      0.019608   0.017242  0.020254   \n",
       "YDL241W               0.000000      0.000000   0.017242  0.006374   \n",
       "YDL240W               0.027946      0.000000   0.008658  0.005102   \n",
       "...                        ...           ...        ...       ...   \n",
       "X21S_rRNA             0.307723      0.038840   0.152908  0.030229   \n",
       "Q0160                 0.004040      0.000000   0.000000  0.000000   \n",
       "Q0275                 0.000000      0.000000   0.000000  0.000000   \n",
       "KANMX                 2.372927      1.500772   2.615808  1.996838   \n",
       "NATMX                 3.187456      1.753067   2.841871  3.260749   \n",
       "\n",
       "           stp2_YPDDiauxic  stp2_YPDRapa  stp2_YPEtOH  \n",
       "YDL248W           0.006494      0.003743     0.000000  \n",
       "YDL244W           0.012945      0.024693     0.061875  \n",
       "YDL243C           0.090263      0.113329     0.068520  \n",
       "YDL241W           0.000000      0.004988     0.000000  \n",
       "YDL240W           0.009725      0.009950     0.027974  \n",
       "...                    ...           ...          ...  \n",
       "X21S_rRNA         1.046233      0.096446     0.923358  \n",
       "Q0160             0.022545      0.000000     0.014085  \n",
       "Q0275             0.006494      0.001249     0.000000  \n",
       "KANMX             1.457781      1.563132     2.406881  \n",
       "NATMX             2.014251      3.067180     3.015396  \n",
       "\n",
       "[5804 rows x 132 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# we need the logcounts\n",
    "df = pd.read_csv(yeast_expression, sep = '\\t', index_col = 0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "# Compute the lioness networks\n",
    "import os\n",
    "if os.path.exists(output_folder+'lioness/networks/'):\n",
    "    print('Path exists, %s' %output_folder+'lioness/networks/')\n",
    "else:\n",
    "    os.mkdir(output_folder+'lioness/')\n",
    "    os.mkdir(output_folder+'lioness/networks/')\n",
    "    \n",
    "    \n",
    "for i in range(len(df.columns)):\n",
    "    temp = pd.DataFrame(data = lioness(df, df.columns[i]), columns = df.index)\n",
    "    temp.index.name = None\n",
    "    temp.columns.name = None\n",
    "    temp.to_hdf(output_folder+'lioness/networks/lioness_%s.h5' %df.columns[i], key = 'lioness')\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Re-read the networks and prepare the top1k net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_AmmoniumSulfate.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_Proline.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_Urea.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_MinimalEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_YPEtOH.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_MinimalGlucose.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_YPDRapa.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_YPDDiauxic.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_Glutamine.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_CStarve.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_YPD.h5\n",
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_Urea.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_Proline.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_MinimalEtOH.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_MinimalEtOH.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_YPEtOH.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_Glutamine.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_Proline.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_Proline.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_AmmoniumSulfate.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_YPD.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_YPD.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_YPEtOH.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_YPDDiauxic.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_Glutamine.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_AmmoniumSulfate.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gcn4_YPD.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_YPDDiauxic.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_CStarve.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gln3_MinimalGlucose.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_Proline.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gzf3_Glutamine.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_YPEtOH.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_YPDRapa.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_gat1_YPDDiauxic.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp2_YPD.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal81_Glutamine.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_Urea.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_stp1_Glutamine.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_WT(ho)_MinimalGlucose.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_Urea.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg1_MinimalGlucose.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal80_YPEtOH.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_dal82_MinimalGlucose.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "../results/pseudobulk_perc02/lioness/networks/lioness_rtg3_AmmoniumSulfate.h5\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_73332/4063108517.py:22: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df_lioness[k] = temp[k]\n"
     ]
    }
   ],
   "source": [
    "#output_folder+'lioness/networks/lioness_%s.h5'\n",
    "netsfn = glob.glob(output_folder+'lioness/networks/lioness_*.h5')\n",
    "df_lioness = get_lioness_dataframe(netsfn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_lioness_dataframe(all_data, sample):\n",
    "    all_cov = all_data.T.corr()\n",
    "    all_minus_q_cov = all_data.drop(sample, axis = 1).T.corr()\n",
    "    lioness = all_data.shape[1] * (all_cov - all_minus_q_cov) + all_minus_q_cov\n",
    "    return(lioness)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>stp1_CStarve</th>\n",
       "      <th>rtg3_MinimalEtOH</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "      <th>gcn4_AmmoniumSulfate</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>stp1_YPEtOH</th>\n",
       "      <th>rtg3_MinimalGlucose</th>\n",
       "      <th>rtg1_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>rtg1_Urea</th>\n",
       "      <th>stp1_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>dal80_Urea</th>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <th>rtg3_AmmoniumSulfate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL244W</td>\n",
       "      <td>-0.326455</td>\n",
       "      <td>-0.183065</td>\n",
       "      <td>-1.454582</td>\n",
       "      <td>0.556953</td>\n",
       "      <td>21.848356</td>\n",
       "      <td>2.974861</td>\n",
       "      <td>-4.088434</td>\n",
       "      <td>0.118830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.521763</td>\n",
       "      <td>-0.034138</td>\n",
       "      <td>0.556953</td>\n",
       "      <td>-0.154630</td>\n",
       "      <td>-0.124578</td>\n",
       "      <td>0.556953</td>\n",
       "      <td>0.556953</td>\n",
       "      <td>0.690581</td>\n",
       "      <td>0.556953</td>\n",
       "      <td>0.556953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL243C</td>\n",
       "      <td>0.309936</td>\n",
       "      <td>-0.465772</td>\n",
       "      <td>-0.281539</td>\n",
       "      <td>0.712555</td>\n",
       "      <td>-0.447760</td>\n",
       "      <td>0.121222</td>\n",
       "      <td>-4.644132</td>\n",
       "      <td>0.252321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.438039</td>\n",
       "      <td>-0.185021</td>\n",
       "      <td>0.712555</td>\n",
       "      <td>-0.150324</td>\n",
       "      <td>-0.229136</td>\n",
       "      <td>0.515937</td>\n",
       "      <td>0.712555</td>\n",
       "      <td>0.516237</td>\n",
       "      <td>0.384148</td>\n",
       "      <td>-0.179146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL241W</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>17.168909</td>\n",
       "      <td>2.298828</td>\n",
       "      <td>-2.473554</td>\n",
       "      <td>0.160586</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.399740</td>\n",
       "      <td>-0.144385</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.170982</td>\n",
       "      <td>-0.123675</td>\n",
       "      <td>-0.514571</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.486775</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.370008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL240W</td>\n",
       "      <td>0.484542</td>\n",
       "      <td>-1.724698</td>\n",
       "      <td>0.294094</td>\n",
       "      <td>0.627717</td>\n",
       "      <td>2.548894</td>\n",
       "      <td>0.353596</td>\n",
       "      <td>9.341677</td>\n",
       "      <td>0.104903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.431570</td>\n",
       "      <td>0.041768</td>\n",
       "      <td>0.627717</td>\n",
       "      <td>0.159782</td>\n",
       "      <td>0.229108</td>\n",
       "      <td>0.342976</td>\n",
       "      <td>0.627717</td>\n",
       "      <td>1.076403</td>\n",
       "      <td>-0.153542</td>\n",
       "      <td>-1.724698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>YDL248W</td>\n",
       "      <td>YDL239C</td>\n",
       "      <td>0.081118</td>\n",
       "      <td>0.117934</td>\n",
       "      <td>0.421013</td>\n",
       "      <td>0.611851</td>\n",
       "      <td>-0.492144</td>\n",
       "      <td>1.388058</td>\n",
       "      <td>-3.647434</td>\n",
       "      <td>0.515972</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382508</td>\n",
       "      <td>-0.166430</td>\n",
       "      <td>0.611851</td>\n",
       "      <td>0.093977</td>\n",
       "      <td>0.011653</td>\n",
       "      <td>0.134997</td>\n",
       "      <td>-1.057821</td>\n",
       "      <td>-0.002295</td>\n",
       "      <td>-0.391151</td>\n",
       "      <td>0.611851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840301</th>\n",
       "      <td>Q0160</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>0.135803</td>\n",
       "      <td>-0.032152</td>\n",
       "      <td>6.718697</td>\n",
       "      <td>0.467504</td>\n",
       "      <td>-0.088142</td>\n",
       "      <td>2.805443</td>\n",
       "      <td>0.062519</td>\n",
       "      <td>0.167971</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.103657</td>\n",
       "      <td>0.175040</td>\n",
       "      <td>-0.308482</td>\n",
       "      <td>-0.238714</td>\n",
       "      <td>-0.296718</td>\n",
       "      <td>-0.565867</td>\n",
       "      <td>-0.091058</td>\n",
       "      <td>0.942554</td>\n",
       "      <td>-0.350704</td>\n",
       "      <td>-0.194481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840302</th>\n",
       "      <td>Q0160</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>0.143163</td>\n",
       "      <td>0.317263</td>\n",
       "      <td>5.031897</td>\n",
       "      <td>0.708689</td>\n",
       "      <td>0.146438</td>\n",
       "      <td>2.042518</td>\n",
       "      <td>-0.101530</td>\n",
       "      <td>-0.400569</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.578459</td>\n",
       "      <td>0.234030</td>\n",
       "      <td>0.072486</td>\n",
       "      <td>-0.326134</td>\n",
       "      <td>-0.379064</td>\n",
       "      <td>-0.142304</td>\n",
       "      <td>-0.243738</td>\n",
       "      <td>0.716768</td>\n",
       "      <td>-0.448780</td>\n",
       "      <td>0.165796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840303</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>KANMX</td>\n",
       "      <td>-0.319160</td>\n",
       "      <td>0.061625</td>\n",
       "      <td>2.332609</td>\n",
       "      <td>0.441364</td>\n",
       "      <td>0.009500</td>\n",
       "      <td>-0.422333</td>\n",
       "      <td>0.145667</td>\n",
       "      <td>0.232961</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.005253</td>\n",
       "      <td>-0.015293</td>\n",
       "      <td>-0.212059</td>\n",
       "      <td>-0.139165</td>\n",
       "      <td>0.823978</td>\n",
       "      <td>-0.501284</td>\n",
       "      <td>0.006737</td>\n",
       "      <td>-0.457460</td>\n",
       "      <td>1.209648</td>\n",
       "      <td>-0.094243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840304</th>\n",
       "      <td>Q0275</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>-0.328284</td>\n",
       "      <td>0.306750</td>\n",
       "      <td>1.823782</td>\n",
       "      <td>0.599507</td>\n",
       "      <td>0.165451</td>\n",
       "      <td>-0.290375</td>\n",
       "      <td>-0.052948</td>\n",
       "      <td>-0.335495</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.512643</td>\n",
       "      <td>-0.126881</td>\n",
       "      <td>0.101916</td>\n",
       "      <td>-0.263320</td>\n",
       "      <td>0.996090</td>\n",
       "      <td>-0.090284</td>\n",
       "      <td>-0.184826</td>\n",
       "      <td>-0.325431</td>\n",
       "      <td>1.529250</td>\n",
       "      <td>0.181851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16840305</th>\n",
       "      <td>KANMX</td>\n",
       "      <td>NATMX</td>\n",
       "      <td>1.453623</td>\n",
       "      <td>0.411097</td>\n",
       "      <td>1.001094</td>\n",
       "      <td>1.126259</td>\n",
       "      <td>0.575794</td>\n",
       "      <td>0.988629</td>\n",
       "      <td>0.666627</td>\n",
       "      <td>-0.044759</td>\n",
       "      <td>...</td>\n",
       "      <td>0.406655</td>\n",
       "      <td>0.765146</td>\n",
       "      <td>0.382296</td>\n",
       "      <td>0.858206</td>\n",
       "      <td>0.916858</td>\n",
       "      <td>0.516085</td>\n",
       "      <td>0.732110</td>\n",
       "      <td>1.039462</td>\n",
       "      <td>0.984010</td>\n",
       "      <td>0.382077</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>16840306 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            gene1    gene2  stp1_CStarve  rtg3_MinimalEtOH  gzf3_YPEtOH  \\\n",
       "0         YDL248W  YDL244W     -0.326455         -0.183065    -1.454582   \n",
       "1         YDL248W  YDL243C      0.309936         -0.465772    -0.281539   \n",
       "2         YDL248W  YDL241W      0.370008          0.370008     0.370008   \n",
       "3         YDL248W  YDL240W      0.484542         -1.724698     0.294094   \n",
       "4         YDL248W  YDL239C      0.081118          0.117934     0.421013   \n",
       "...           ...      ...           ...               ...          ...   \n",
       "16840301    Q0160    KANMX      0.135803         -0.032152     6.718697   \n",
       "16840302    Q0160    NATMX      0.143163          0.317263     5.031897   \n",
       "16840303    Q0275    KANMX     -0.319160          0.061625     2.332609   \n",
       "16840304    Q0275    NATMX     -0.328284          0.306750     1.823782   \n",
       "16840305    KANMX    NATMX      1.453623          0.411097     1.001094   \n",
       "\n",
       "          gcn4_AmmoniumSulfate  dal81_MinimalEtOH  stp1_YPEtOH  \\\n",
       "0                     0.556953          21.848356     2.974861   \n",
       "1                     0.712555          -0.447760     0.121222   \n",
       "2                     0.370008          17.168909     2.298828   \n",
       "3                     0.627717           2.548894     0.353596   \n",
       "4                     0.611851          -0.492144     1.388058   \n",
       "...                        ...                ...          ...   \n",
       "16840301              0.467504          -0.088142     2.805443   \n",
       "16840302              0.708689           0.146438     2.042518   \n",
       "16840303              0.441364           0.009500    -0.422333   \n",
       "16840304              0.599507           0.165451    -0.290375   \n",
       "16840305              1.126259           0.575794     0.988629   \n",
       "\n",
       "          rtg3_MinimalGlucose  rtg1_YPDRapa  ...  stp2_YPD  dal81_Glutamine  \\\n",
       "0                   -4.088434      0.118830  ...  0.521763        -0.034138   \n",
       "1                   -4.644132      0.252321  ...  0.438039        -0.185021   \n",
       "2                   -2.473554      0.160586  ... -0.399740        -0.144385   \n",
       "3                    9.341677      0.104903  ...  0.431570         0.041768   \n",
       "4                   -3.647434      0.515972  ...  0.382508        -0.166430   \n",
       "...                       ...           ...  ...       ...              ...   \n",
       "16840301             0.062519      0.167971  ... -0.103657         0.175040   \n",
       "16840302            -0.101530     -0.400569  ... -0.578459         0.234030   \n",
       "16840303             0.145667      0.232961  ... -0.005253        -0.015293   \n",
       "16840304            -0.052948     -0.335495  ... -0.512643        -0.126881   \n",
       "16840305             0.666627     -0.044759  ...  0.406655         0.765146   \n",
       "\n",
       "          rtg1_Urea  stp1_Glutamine  WT(ho)_MinimalGlucose  dal80_Urea  \\\n",
       "0          0.556953       -0.154630              -0.124578    0.556953   \n",
       "1          0.712555       -0.150324              -0.229136    0.515937   \n",
       "2          0.370008        0.170982              -0.123675   -0.514571   \n",
       "3          0.627717        0.159782               0.229108    0.342976   \n",
       "4          0.611851        0.093977               0.011653    0.134997   \n",
       "...             ...             ...                    ...         ...   \n",
       "16840301  -0.308482       -0.238714              -0.296718   -0.565867   \n",
       "16840302   0.072486       -0.326134              -0.379064   -0.142304   \n",
       "16840303  -0.212059       -0.139165               0.823978   -0.501284   \n",
       "16840304   0.101916       -0.263320               0.996090   -0.090284   \n",
       "16840305   0.382296        0.858206               0.916858    0.516085   \n",
       "\n",
       "          rtg1_MinimalGlucose  dal80_YPEtOH  dal82_MinimalGlucose  \\\n",
       "0                    0.556953      0.690581              0.556953   \n",
       "1                    0.712555      0.516237              0.384148   \n",
       "2                    0.370008      0.486775              0.370008   \n",
       "3                    0.627717      1.076403             -0.153542   \n",
       "4                   -1.057821     -0.002295             -0.391151   \n",
       "...                       ...           ...                   ...   \n",
       "16840301            -0.091058      0.942554             -0.350704   \n",
       "16840302            -0.243738      0.716768             -0.448780   \n",
       "16840303             0.006737     -0.457460              1.209648   \n",
       "16840304            -0.184826     -0.325431              1.529250   \n",
       "16840305             0.732110      1.039462              0.984010   \n",
       "\n",
       "          rtg3_AmmoniumSulfate  \n",
       "0                     0.556953  \n",
       "1                    -0.179146  \n",
       "2                     0.370008  \n",
       "3                    -1.724698  \n",
       "4                     0.611851  \n",
       "...                        ...  \n",
       "16840301             -0.194481  \n",
       "16840302              0.165796  \n",
       "16840303             -0.094243  \n",
       "16840304              0.181851  \n",
       "16840305              0.382077  \n",
       "\n",
       "[16840306 rows x 134 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_lioness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YDL248W</th>\n",
       "      <th>YDL247W</th>\n",
       "      <th>YDL246C</th>\n",
       "      <th>YDL245C</th>\n",
       "      <th>YDL244W</th>\n",
       "      <th>YDL243C</th>\n",
       "      <th>YDL242W</th>\n",
       "      <th>YDL241W</th>\n",
       "      <th>YDL240W</th>\n",
       "      <th>YDL239C</th>\n",
       "      <th>...</th>\n",
       "      <th>Q0130</th>\n",
       "      <th>Q0140</th>\n",
       "      <th>X21S_rRNA</th>\n",
       "      <th>Q0160</th>\n",
       "      <th>Q0250</th>\n",
       "      <th>Q0255</th>\n",
       "      <th>Q0275</th>\n",
       "      <th>RPM1</th>\n",
       "      <th>KANMX</th>\n",
       "      <th>NATMX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>YDL248W</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059980</td>\n",
       "      <td>0.017953</td>\n",
       "      <td>0.037624</td>\n",
       "      <td>-0.326455</td>\n",
       "      <td>0.309936</td>\n",
       "      <td>0.105632</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.484542</td>\n",
       "      <td>0.081118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475165</td>\n",
       "      <td>0.077800</td>\n",
       "      <td>0.461080</td>\n",
       "      <td>0.019695</td>\n",
       "      <td>-0.426582</td>\n",
       "      <td>-0.576932</td>\n",
       "      <td>-0.145729</td>\n",
       "      <td>0.047878</td>\n",
       "      <td>0.903296</td>\n",
       "      <td>0.994281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL247W</th>\n",
       "      <td>0.059980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.024746</td>\n",
       "      <td>-0.062087</td>\n",
       "      <td>0.149972</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.051016</td>\n",
       "      <td>0.233078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198918</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.170517</td>\n",
       "      <td>0.153139</td>\n",
       "      <td>0.009965</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.100616</td>\n",
       "      <td>0.719141</td>\n",
       "      <td>0.188378</td>\n",
       "      <td>0.187686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL246C</th>\n",
       "      <td>0.017953</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005143</td>\n",
       "      <td>-0.067863</td>\n",
       "      <td>0.015918</td>\n",
       "      <td>0.042134</td>\n",
       "      <td>0.112289</td>\n",
       "      <td>0.070911</td>\n",
       "      <td>0.008712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028142</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.040694</td>\n",
       "      <td>-0.029077</td>\n",
       "      <td>-0.094587</td>\n",
       "      <td>0.094154</td>\n",
       "      <td>0.101685</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.131426</td>\n",
       "      <td>0.145803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL245C</th>\n",
       "      <td>0.037624</td>\n",
       "      <td>0.024746</td>\n",
       "      <td>0.005143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036888</td>\n",
       "      <td>0.351655</td>\n",
       "      <td>0.014480</td>\n",
       "      <td>0.092496</td>\n",
       "      <td>0.288280</td>\n",
       "      <td>0.135403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141207</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.137391</td>\n",
       "      <td>-0.044835</td>\n",
       "      <td>-0.159012</td>\n",
       "      <td>-0.168490</td>\n",
       "      <td>-0.079890</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.257979</td>\n",
       "      <td>0.272165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YDL244W</th>\n",
       "      <td>-0.326455</td>\n",
       "      <td>-0.062087</td>\n",
       "      <td>-0.067863</td>\n",
       "      <td>-0.036888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.108929</td>\n",
       "      <td>-0.030217</td>\n",
       "      <td>-0.316204</td>\n",
       "      <td>-0.419551</td>\n",
       "      <td>0.075105</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268529</td>\n",
       "      <td>-0.043443</td>\n",
       "      <td>-0.238516</td>\n",
       "      <td>0.166045</td>\n",
       "      <td>0.575521</td>\n",
       "      <td>0.675285</td>\n",
       "      <td>0.241473</td>\n",
       "      <td>-0.128044</td>\n",
       "      <td>-1.152036</td>\n",
       "      <td>-1.183636</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0255</th>\n",
       "      <td>-0.576932</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.094154</td>\n",
       "      <td>-0.168490</td>\n",
       "      <td>0.675285</td>\n",
       "      <td>-0.434113</td>\n",
       "      <td>-0.120610</td>\n",
       "      <td>-0.565906</td>\n",
       "      <td>-0.650681</td>\n",
       "      <td>-0.134463</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.582401</td>\n",
       "      <td>-0.184205</td>\n",
       "      <td>-0.575358</td>\n",
       "      <td>-0.025305</td>\n",
       "      <td>0.741540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257527</td>\n",
       "      <td>-0.088455</td>\n",
       "      <td>-1.449287</td>\n",
       "      <td>-1.508559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Q0275</th>\n",
       "      <td>-0.145729</td>\n",
       "      <td>0.100616</td>\n",
       "      <td>0.101685</td>\n",
       "      <td>-0.079890</td>\n",
       "      <td>0.241473</td>\n",
       "      <td>0.143542</td>\n",
       "      <td>-0.053248</td>\n",
       "      <td>-0.200817</td>\n",
       "      <td>-0.171002</td>\n",
       "      <td>0.258779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055737</td>\n",
       "      <td>-0.077215</td>\n",
       "      <td>0.088178</td>\n",
       "      <td>0.176080</td>\n",
       "      <td>0.151593</td>\n",
       "      <td>0.257527</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>-0.319160</td>\n",
       "      <td>-0.328284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RPM1</th>\n",
       "      <td>0.047878</td>\n",
       "      <td>0.719141</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>-0.128044</td>\n",
       "      <td>0.173492</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.078292</td>\n",
       "      <td>0.090487</td>\n",
       "      <td>0.217101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.262765</td>\n",
       "      <td>0.059867</td>\n",
       "      <td>0.237530</td>\n",
       "      <td>0.143923</td>\n",
       "      <td>0.031125</td>\n",
       "      <td>-0.088455</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263548</td>\n",
       "      <td>0.264069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KANMX</th>\n",
       "      <td>0.903296</td>\n",
       "      <td>0.188378</td>\n",
       "      <td>0.131426</td>\n",
       "      <td>0.257979</td>\n",
       "      <td>-1.152036</td>\n",
       "      <td>0.849381</td>\n",
       "      <td>0.221006</td>\n",
       "      <td>0.928970</td>\n",
       "      <td>0.941555</td>\n",
       "      <td>0.373282</td>\n",
       "      <td>...</td>\n",
       "      <td>1.086083</td>\n",
       "      <td>0.251198</td>\n",
       "      <td>1.030257</td>\n",
       "      <td>0.135803</td>\n",
       "      <td>-1.182438</td>\n",
       "      <td>-1.449287</td>\n",
       "      <td>-0.319160</td>\n",
       "      <td>0.263548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.453623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NATMX</th>\n",
       "      <td>0.994281</td>\n",
       "      <td>0.187686</td>\n",
       "      <td>0.145803</td>\n",
       "      <td>0.272165</td>\n",
       "      <td>-1.183636</td>\n",
       "      <td>0.872173</td>\n",
       "      <td>0.185732</td>\n",
       "      <td>0.839455</td>\n",
       "      <td>1.041482</td>\n",
       "      <td>0.324507</td>\n",
       "      <td>...</td>\n",
       "      <td>1.253661</td>\n",
       "      <td>0.284591</td>\n",
       "      <td>1.218144</td>\n",
       "      <td>0.143163</td>\n",
       "      <td>-1.248899</td>\n",
       "      <td>-1.508559</td>\n",
       "      <td>-0.328284</td>\n",
       "      <td>0.264069</td>\n",
       "      <td>1.453623</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6520 rows × 6520 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          YDL248W   YDL247W   YDL246C   YDL245C   YDL244W   YDL243C   YDL242W  \\\n",
       "YDL248W  1.000000  0.059980  0.017953  0.037624 -0.326455  0.309936  0.105632   \n",
       "YDL247W  0.059980  1.000000  0.000110  0.024746 -0.062087  0.149972  0.000147   \n",
       "YDL246C  0.017953  0.000110  1.000000  0.005143 -0.067863  0.015918  0.042134   \n",
       "YDL245C  0.037624  0.024746  0.005143  1.000000 -0.036888  0.351655  0.014480   \n",
       "YDL244W -0.326455 -0.062087 -0.067863 -0.036888  1.000000 -0.108929 -0.030217   \n",
       "...           ...       ...       ...       ...       ...       ...       ...   \n",
       "Q0255   -0.576932  0.003383  0.094154 -0.168490  0.675285 -0.434113 -0.120610   \n",
       "Q0275   -0.145729  0.100616  0.101685 -0.079890  0.241473  0.143542 -0.053248   \n",
       "RPM1     0.047878  0.719141  0.000262  0.000657 -0.128044  0.173492  0.000332   \n",
       "KANMX    0.903296  0.188378  0.131426  0.257979 -1.152036  0.849381  0.221006   \n",
       "NATMX    0.994281  0.187686  0.145803  0.272165 -1.183636  0.872173  0.185732   \n",
       "\n",
       "          YDL241W   YDL240W   YDL239C  ...     Q0130     Q0140  X21S_rRNA  \\\n",
       "YDL248W  0.370008  0.484542  0.081118  ...  0.475165  0.077800   0.461080   \n",
       "YDL247W  0.061538  0.051016  0.233078  ...  0.198918  0.000384   0.170517   \n",
       "YDL246C  0.112289  0.070911  0.008712  ...  0.028142  0.000332   0.040694   \n",
       "YDL245C  0.092496  0.288280  0.135403  ...  0.141207  0.000795   0.137391   \n",
       "YDL244W -0.316204 -0.419551  0.075105  ... -0.268529 -0.043443  -0.238516   \n",
       "...           ...       ...       ...  ...       ...       ...        ...   \n",
       "Q0255   -0.565906 -0.650681 -0.134463  ... -0.582401 -0.184205  -0.575358   \n",
       "Q0275   -0.200817 -0.171002  0.258779  ...  0.055737 -0.077215   0.088178   \n",
       "RPM1     0.078292  0.090487  0.217101  ...  0.262765  0.059867   0.237530   \n",
       "KANMX    0.928970  0.941555  0.373282  ...  1.086083  0.251198   1.030257   \n",
       "NATMX    0.839455  1.041482  0.324507  ...  1.253661  0.284591   1.218144   \n",
       "\n",
       "            Q0160     Q0250     Q0255     Q0275      RPM1     KANMX     NATMX  \n",
       "YDL248W  0.019695 -0.426582 -0.576932 -0.145729  0.047878  0.903296  0.994281  \n",
       "YDL247W  0.153139  0.009965  0.003383  0.100616  0.719141  0.188378  0.187686  \n",
       "YDL246C -0.029077 -0.094587  0.094154  0.101685  0.000262  0.131426  0.145803  \n",
       "YDL245C -0.044835 -0.159012 -0.168490 -0.079890  0.000657  0.257979  0.272165  \n",
       "YDL244W  0.166045  0.575521  0.675285  0.241473 -0.128044 -1.152036 -1.183636  \n",
       "...           ...       ...       ...       ...       ...       ...       ...  \n",
       "Q0255   -0.025305  0.741540  1.000000  0.257527 -0.088455 -1.449287 -1.508559  \n",
       "Q0275    0.176080  0.151593  0.257527  1.000000  0.038347 -0.319160 -0.328284  \n",
       "RPM1     0.143923  0.031125 -0.088455  0.038347  1.000000  0.263548  0.264069  \n",
       "KANMX    0.135803 -1.182438 -1.449287 -0.319160  0.263548  1.000000  1.453623  \n",
       "NATMX    0.143163 -1.248899 -1.508559 -0.328284  0.264069  1.453623  1.000000  \n",
       "\n",
       "[6520 rows x 6520 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute_lioness_dataframe(df_expression, netsfn[0].split('/')[-1][8:-3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>YDL248W</th>\n",
       "      <th>YDL247W</th>\n",
       "      <th>YDL246C</th>\n",
       "      <th>YDL245C</th>\n",
       "      <th>YDL244W</th>\n",
       "      <th>YDL243C</th>\n",
       "      <th>YDL242W</th>\n",
       "      <th>YDL241W</th>\n",
       "      <th>YDL240W</th>\n",
       "      <th>YDL239C</th>\n",
       "      <th>...</th>\n",
       "      <th>Q0130</th>\n",
       "      <th>Q0140</th>\n",
       "      <th>X21S_rRNA</th>\n",
       "      <th>Q0160</th>\n",
       "      <th>Q0250</th>\n",
       "      <th>Q0255</th>\n",
       "      <th>Q0275</th>\n",
       "      <th>RPM1</th>\n",
       "      <th>KANMX</th>\n",
       "      <th>NATMX</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059980</td>\n",
       "      <td>0.017953</td>\n",
       "      <td>0.037624</td>\n",
       "      <td>-0.326455</td>\n",
       "      <td>0.309936</td>\n",
       "      <td>0.105632</td>\n",
       "      <td>0.370008</td>\n",
       "      <td>0.484542</td>\n",
       "      <td>0.081118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.475165</td>\n",
       "      <td>0.077800</td>\n",
       "      <td>0.461080</td>\n",
       "      <td>0.019695</td>\n",
       "      <td>-0.426582</td>\n",
       "      <td>-0.576932</td>\n",
       "      <td>-0.145729</td>\n",
       "      <td>0.047878</td>\n",
       "      <td>0.903296</td>\n",
       "      <td>0.994281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.059980</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.024746</td>\n",
       "      <td>-0.062087</td>\n",
       "      <td>0.149972</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.061538</td>\n",
       "      <td>0.051016</td>\n",
       "      <td>0.233078</td>\n",
       "      <td>...</td>\n",
       "      <td>0.198918</td>\n",
       "      <td>0.000384</td>\n",
       "      <td>0.170517</td>\n",
       "      <td>0.153139</td>\n",
       "      <td>0.009965</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.100616</td>\n",
       "      <td>0.719141</td>\n",
       "      <td>0.188378</td>\n",
       "      <td>0.187686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.017953</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005143</td>\n",
       "      <td>-0.067863</td>\n",
       "      <td>0.015918</td>\n",
       "      <td>0.042134</td>\n",
       "      <td>0.112289</td>\n",
       "      <td>0.070911</td>\n",
       "      <td>0.008712</td>\n",
       "      <td>...</td>\n",
       "      <td>0.028142</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.040694</td>\n",
       "      <td>-0.029077</td>\n",
       "      <td>-0.094587</td>\n",
       "      <td>0.094154</td>\n",
       "      <td>0.101685</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.131426</td>\n",
       "      <td>0.145803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.037624</td>\n",
       "      <td>0.024746</td>\n",
       "      <td>0.005143</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.036888</td>\n",
       "      <td>0.351655</td>\n",
       "      <td>0.014480</td>\n",
       "      <td>0.092496</td>\n",
       "      <td>0.288280</td>\n",
       "      <td>0.135403</td>\n",
       "      <td>...</td>\n",
       "      <td>0.141207</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.137391</td>\n",
       "      <td>-0.044835</td>\n",
       "      <td>-0.159012</td>\n",
       "      <td>-0.168490</td>\n",
       "      <td>-0.079890</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>0.257979</td>\n",
       "      <td>0.272165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.326455</td>\n",
       "      <td>-0.062087</td>\n",
       "      <td>-0.067863</td>\n",
       "      <td>-0.036888</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.108929</td>\n",
       "      <td>-0.030217</td>\n",
       "      <td>-0.316204</td>\n",
       "      <td>-0.419551</td>\n",
       "      <td>0.075105</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.268529</td>\n",
       "      <td>-0.043443</td>\n",
       "      <td>-0.238516</td>\n",
       "      <td>0.166045</td>\n",
       "      <td>0.575521</td>\n",
       "      <td>0.675285</td>\n",
       "      <td>0.241473</td>\n",
       "      <td>-0.128044</td>\n",
       "      <td>-1.152036</td>\n",
       "      <td>-1.183636</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>6515</th>\n",
       "      <td>-0.576932</td>\n",
       "      <td>0.003383</td>\n",
       "      <td>0.094154</td>\n",
       "      <td>-0.168490</td>\n",
       "      <td>0.675285</td>\n",
       "      <td>-0.434113</td>\n",
       "      <td>-0.120610</td>\n",
       "      <td>-0.565906</td>\n",
       "      <td>-0.650681</td>\n",
       "      <td>-0.134463</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.582401</td>\n",
       "      <td>-0.184205</td>\n",
       "      <td>-0.575358</td>\n",
       "      <td>-0.025305</td>\n",
       "      <td>0.741540</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.257527</td>\n",
       "      <td>-0.088455</td>\n",
       "      <td>-1.449287</td>\n",
       "      <td>-1.508559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6516</th>\n",
       "      <td>-0.145729</td>\n",
       "      <td>0.100616</td>\n",
       "      <td>0.101685</td>\n",
       "      <td>-0.079890</td>\n",
       "      <td>0.241473</td>\n",
       "      <td>0.143542</td>\n",
       "      <td>-0.053248</td>\n",
       "      <td>-0.200817</td>\n",
       "      <td>-0.171002</td>\n",
       "      <td>0.258779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.055737</td>\n",
       "      <td>-0.077215</td>\n",
       "      <td>0.088178</td>\n",
       "      <td>0.176080</td>\n",
       "      <td>0.151593</td>\n",
       "      <td>0.257527</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>-0.319160</td>\n",
       "      <td>-0.328284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6517</th>\n",
       "      <td>0.047878</td>\n",
       "      <td>0.719141</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000657</td>\n",
       "      <td>-0.128044</td>\n",
       "      <td>0.173492</td>\n",
       "      <td>0.000332</td>\n",
       "      <td>0.078292</td>\n",
       "      <td>0.090487</td>\n",
       "      <td>0.217101</td>\n",
       "      <td>...</td>\n",
       "      <td>0.262765</td>\n",
       "      <td>0.059867</td>\n",
       "      <td>0.237530</td>\n",
       "      <td>0.143923</td>\n",
       "      <td>0.031125</td>\n",
       "      <td>-0.088455</td>\n",
       "      <td>0.038347</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.263548</td>\n",
       "      <td>0.264069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6518</th>\n",
       "      <td>0.903296</td>\n",
       "      <td>0.188378</td>\n",
       "      <td>0.131426</td>\n",
       "      <td>0.257979</td>\n",
       "      <td>-1.152036</td>\n",
       "      <td>0.849381</td>\n",
       "      <td>0.221006</td>\n",
       "      <td>0.928970</td>\n",
       "      <td>0.941555</td>\n",
       "      <td>0.373282</td>\n",
       "      <td>...</td>\n",
       "      <td>1.086083</td>\n",
       "      <td>0.251198</td>\n",
       "      <td>1.030257</td>\n",
       "      <td>0.135803</td>\n",
       "      <td>-1.182438</td>\n",
       "      <td>-1.449287</td>\n",
       "      <td>-0.319160</td>\n",
       "      <td>0.263548</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.453623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6519</th>\n",
       "      <td>0.994281</td>\n",
       "      <td>0.187686</td>\n",
       "      <td>0.145803</td>\n",
       "      <td>0.272165</td>\n",
       "      <td>-1.183636</td>\n",
       "      <td>0.872173</td>\n",
       "      <td>0.185732</td>\n",
       "      <td>0.839455</td>\n",
       "      <td>1.041482</td>\n",
       "      <td>0.324507</td>\n",
       "      <td>...</td>\n",
       "      <td>1.253661</td>\n",
       "      <td>0.284591</td>\n",
       "      <td>1.218144</td>\n",
       "      <td>0.143163</td>\n",
       "      <td>-1.248899</td>\n",
       "      <td>-1.508559</td>\n",
       "      <td>-0.328284</td>\n",
       "      <td>0.264069</td>\n",
       "      <td>1.453623</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6520 rows × 6520 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       YDL248W   YDL247W   YDL246C   YDL245C   YDL244W   YDL243C   YDL242W  \\\n",
       "0     1.000000  0.059980  0.017953  0.037624 -0.326455  0.309936  0.105632   \n",
       "1     0.059980  1.000000  0.000110  0.024746 -0.062087  0.149972  0.000147   \n",
       "2     0.017953  0.000110  1.000000  0.005143 -0.067863  0.015918  0.042134   \n",
       "3     0.037624  0.024746  0.005143  1.000000 -0.036888  0.351655  0.014480   \n",
       "4    -0.326455 -0.062087 -0.067863 -0.036888  1.000000 -0.108929 -0.030217   \n",
       "...        ...       ...       ...       ...       ...       ...       ...   \n",
       "6515 -0.576932  0.003383  0.094154 -0.168490  0.675285 -0.434113 -0.120610   \n",
       "6516 -0.145729  0.100616  0.101685 -0.079890  0.241473  0.143542 -0.053248   \n",
       "6517  0.047878  0.719141  0.000262  0.000657 -0.128044  0.173492  0.000332   \n",
       "6518  0.903296  0.188378  0.131426  0.257979 -1.152036  0.849381  0.221006   \n",
       "6519  0.994281  0.187686  0.145803  0.272165 -1.183636  0.872173  0.185732   \n",
       "\n",
       "       YDL241W   YDL240W   YDL239C  ...     Q0130     Q0140  X21S_rRNA  \\\n",
       "0     0.370008  0.484542  0.081118  ...  0.475165  0.077800   0.461080   \n",
       "1     0.061538  0.051016  0.233078  ...  0.198918  0.000384   0.170517   \n",
       "2     0.112289  0.070911  0.008712  ...  0.028142  0.000332   0.040694   \n",
       "3     0.092496  0.288280  0.135403  ...  0.141207  0.000795   0.137391   \n",
       "4    -0.316204 -0.419551  0.075105  ... -0.268529 -0.043443  -0.238516   \n",
       "...        ...       ...       ...  ...       ...       ...        ...   \n",
       "6515 -0.565906 -0.650681 -0.134463  ... -0.582401 -0.184205  -0.575358   \n",
       "6516 -0.200817 -0.171002  0.258779  ...  0.055737 -0.077215   0.088178   \n",
       "6517  0.078292  0.090487  0.217101  ...  0.262765  0.059867   0.237530   \n",
       "6518  0.928970  0.941555  0.373282  ...  1.086083  0.251198   1.030257   \n",
       "6519  0.839455  1.041482  0.324507  ...  1.253661  0.284591   1.218144   \n",
       "\n",
       "         Q0160     Q0250     Q0255     Q0275      RPM1     KANMX     NATMX  \n",
       "0     0.019695 -0.426582 -0.576932 -0.145729  0.047878  0.903296  0.994281  \n",
       "1     0.153139  0.009965  0.003383  0.100616  0.719141  0.188378  0.187686  \n",
       "2    -0.029077 -0.094587  0.094154  0.101685  0.000262  0.131426  0.145803  \n",
       "3    -0.044835 -0.159012 -0.168490 -0.079890  0.000657  0.257979  0.272165  \n",
       "4     0.166045  0.575521  0.675285  0.241473 -0.128044 -1.152036 -1.183636  \n",
       "...        ...       ...       ...       ...       ...       ...       ...  \n",
       "6515 -0.025305  0.741540  1.000000  0.257527 -0.088455 -1.449287 -1.508559  \n",
       "6516  0.176080  0.151593  0.257527  1.000000  0.038347 -0.319160 -0.328284  \n",
       "6517  0.143923  0.031125 -0.088455  0.038347  1.000000  0.263548  0.264069  \n",
       "6518  0.135803 -1.182438 -1.449287 -0.319160  0.263548  1.000000  1.453623  \n",
       "6519  0.143163 -1.248899 -1.508559 -0.328284  0.264069  1.453623  1.000000  \n",
       "\n",
       "[6520 rows x 6520 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new_lioness = pd.read_hdf(netsfn[0], key = 'lioness')\n",
    "#new_lioness"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_all = df_lioness\n",
    "\n",
    "# Get top 1k edges\n",
    "mean = df_all.iloc[:,2:].mean(axis = 1)\n",
    "mean.sort_values(ascending = False, inplace = True)\n",
    "# Get top 1K\n",
    "top_1k = np.r_[mean.index.values[:500], mean.index.values[-500:]]\n",
    "df_top1k = df_all.iloc[top_1k, :]\n",
    "#df_top1k\n",
    "df_top1k.to_csv(output_folder+'lioness/networks/lioness_top1k.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "random_1k = np.random.choice(df_all.index, 1000, replace = False)\n",
    "df_random_1k = df_all.loc[random_1k, :]\n",
    "df_random_1k.to_csv(output_folder+'lioness/networks/lioness_random1k.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>gene1</th>\n",
       "      <th>gene2</th>\n",
       "      <th>stp1_CStarve</th>\n",
       "      <th>rtg3_MinimalEtOH</th>\n",
       "      <th>gzf3_YPEtOH</th>\n",
       "      <th>gcn4_AmmoniumSulfate</th>\n",
       "      <th>dal81_MinimalEtOH</th>\n",
       "      <th>stp1_YPEtOH</th>\n",
       "      <th>rtg3_MinimalGlucose</th>\n",
       "      <th>rtg1_YPDRapa</th>\n",
       "      <th>...</th>\n",
       "      <th>stp2_YPD</th>\n",
       "      <th>dal81_Glutamine</th>\n",
       "      <th>rtg1_Urea</th>\n",
       "      <th>stp1_Glutamine</th>\n",
       "      <th>WT(ho)_MinimalGlucose</th>\n",
       "      <th>dal80_Urea</th>\n",
       "      <th>rtg1_MinimalGlucose</th>\n",
       "      <th>dal80_YPEtOH</th>\n",
       "      <th>dal82_MinimalGlucose</th>\n",
       "      <th>rtg3_AmmoniumSulfate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12889508</th>\n",
       "      <td>YMR118C</td>\n",
       "      <td>YKL096W.A</td>\n",
       "      <td>-1.728102</td>\n",
       "      <td>-0.837744</td>\n",
       "      <td>-0.883231</td>\n",
       "      <td>-0.906175</td>\n",
       "      <td>-0.605244</td>\n",
       "      <td>-0.921549</td>\n",
       "      <td>-0.924005</td>\n",
       "      <td>-0.870742</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.645092</td>\n",
       "      <td>-0.918537</td>\n",
       "      <td>-0.886700</td>\n",
       "      <td>-0.894064</td>\n",
       "      <td>-0.837485</td>\n",
       "      <td>-0.913223</td>\n",
       "      <td>-0.926309</td>\n",
       "      <td>-0.874747</td>\n",
       "      <td>-0.906063</td>\n",
       "      <td>-0.916240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3952068</th>\n",
       "      <td>YDR524C.B</td>\n",
       "      <td>YMR118C</td>\n",
       "      <td>-1.444611</td>\n",
       "      <td>-0.879595</td>\n",
       "      <td>-0.905551</td>\n",
       "      <td>-0.923734</td>\n",
       "      <td>-0.865930</td>\n",
       "      <td>-0.891682</td>\n",
       "      <td>-0.926598</td>\n",
       "      <td>-0.926686</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.899044</td>\n",
       "      <td>-0.913950</td>\n",
       "      <td>-0.933895</td>\n",
       "      <td>-0.873018</td>\n",
       "      <td>-0.853778</td>\n",
       "      <td>-0.860426</td>\n",
       "      <td>-0.933526</td>\n",
       "      <td>-0.912958</td>\n",
       "      <td>-0.907015</td>\n",
       "      <td>-0.926957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9191547</th>\n",
       "      <td>YLR044C</td>\n",
       "      <td>YMR175W</td>\n",
       "      <td>-0.993298</td>\n",
       "      <td>-0.796037</td>\n",
       "      <td>-0.913164</td>\n",
       "      <td>-0.815117</td>\n",
       "      <td>-0.983536</td>\n",
       "      <td>-0.877985</td>\n",
       "      <td>-0.942212</td>\n",
       "      <td>-0.961324</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.959546</td>\n",
       "      <td>-0.885956</td>\n",
       "      <td>-0.870323</td>\n",
       "      <td>-0.841308</td>\n",
       "      <td>-0.929227</td>\n",
       "      <td>-0.962200</td>\n",
       "      <td>-0.946782</td>\n",
       "      <td>-0.872898</td>\n",
       "      <td>-0.932973</td>\n",
       "      <td>-0.861574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9457239</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YHR139C</td>\n",
       "      <td>-1.714633</td>\n",
       "      <td>-0.738971</td>\n",
       "      <td>-0.929638</td>\n",
       "      <td>-0.895104</td>\n",
       "      <td>-0.920787</td>\n",
       "      <td>-0.923613</td>\n",
       "      <td>-0.928609</td>\n",
       "      <td>-0.916766</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.924994</td>\n",
       "      <td>-0.906663</td>\n",
       "      <td>-0.920949</td>\n",
       "      <td>-0.901382</td>\n",
       "      <td>-0.855765</td>\n",
       "      <td>-0.902125</td>\n",
       "      <td>-0.927273</td>\n",
       "      <td>-0.913120</td>\n",
       "      <td>-0.868750</td>\n",
       "      <td>-0.924576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9455091</th>\n",
       "      <td>YLR110C</td>\n",
       "      <td>YMR118C</td>\n",
       "      <td>-1.367036</td>\n",
       "      <td>-0.958905</td>\n",
       "      <td>-0.965489</td>\n",
       "      <td>-0.954177</td>\n",
       "      <td>-0.931349</td>\n",
       "      <td>-0.962897</td>\n",
       "      <td>-0.966489</td>\n",
       "      <td>-0.957215</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.943470</td>\n",
       "      <td>-0.962405</td>\n",
       "      <td>-0.935679</td>\n",
       "      <td>-0.957471</td>\n",
       "      <td>-0.895982</td>\n",
       "      <td>-0.957122</td>\n",
       "      <td>-0.967120</td>\n",
       "      <td>-0.963707</td>\n",
       "      <td>-0.936252</td>\n",
       "      <td>-0.942690</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 134 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              gene1      gene2  stp1_CStarve  rtg3_MinimalEtOH  gzf3_YPEtOH  \\\n",
       "12889508    YMR118C  YKL096W.A     -1.728102         -0.837744    -0.883231   \n",
       "3952068   YDR524C.B    YMR118C     -1.444611         -0.879595    -0.905551   \n",
       "9191547     YLR044C    YMR175W     -0.993298         -0.796037    -0.913164   \n",
       "9457239     YLR110C    YHR139C     -1.714633         -0.738971    -0.929638   \n",
       "9455091     YLR110C    YMR118C     -1.367036         -0.958905    -0.965489   \n",
       "\n",
       "          gcn4_AmmoniumSulfate  dal81_MinimalEtOH  stp1_YPEtOH  \\\n",
       "12889508             -0.906175          -0.605244    -0.921549   \n",
       "3952068              -0.923734          -0.865930    -0.891682   \n",
       "9191547              -0.815117          -0.983536    -0.877985   \n",
       "9457239              -0.895104          -0.920787    -0.923613   \n",
       "9455091              -0.954177          -0.931349    -0.962897   \n",
       "\n",
       "          rtg3_MinimalGlucose  rtg1_YPDRapa  ...  stp2_YPD  dal81_Glutamine  \\\n",
       "12889508            -0.924005     -0.870742  ... -0.645092        -0.918537   \n",
       "3952068             -0.926598     -0.926686  ... -0.899044        -0.913950   \n",
       "9191547             -0.942212     -0.961324  ... -0.959546        -0.885956   \n",
       "9457239             -0.928609     -0.916766  ... -0.924994        -0.906663   \n",
       "9455091             -0.966489     -0.957215  ... -0.943470        -0.962405   \n",
       "\n",
       "          rtg1_Urea  stp1_Glutamine  WT(ho)_MinimalGlucose  dal80_Urea  \\\n",
       "12889508  -0.886700       -0.894064              -0.837485   -0.913223   \n",
       "3952068   -0.933895       -0.873018              -0.853778   -0.860426   \n",
       "9191547   -0.870323       -0.841308              -0.929227   -0.962200   \n",
       "9457239   -0.920949       -0.901382              -0.855765   -0.902125   \n",
       "9455091   -0.935679       -0.957471              -0.895982   -0.957122   \n",
       "\n",
       "          rtg1_MinimalGlucose  dal80_YPEtOH  dal82_MinimalGlucose  \\\n",
       "12889508            -0.926309     -0.874747             -0.906063   \n",
       "3952068             -0.933526     -0.912958             -0.907015   \n",
       "9191547             -0.946782     -0.872898             -0.932973   \n",
       "9457239             -0.927273     -0.913120             -0.868750   \n",
       "9455091             -0.967120     -0.963707             -0.936252   \n",
       "\n",
       "          rtg3_AmmoniumSulfate  \n",
       "12889508             -0.916240  \n",
       "3952068              -0.926957  \n",
       "9191547              -0.861574  \n",
       "9457239              -0.924576  \n",
       "9455091              -0.942690  \n",
       "\n",
       "[5 rows x 134 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_top1k.tail()"
   ]
  }
 ],
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